{
  "source_file": "0930",
  "source_platform": "Medium",
  "author": "Chua Chin Hon",
  "title_listed": "Practical NLP: Summarising Short and Long Speeches With Hugging Face's Pipeline",
  "keywords": "",
  "domain": "文本处理",
  "content_type": "深度技术解析",
  "is_paid_flag": "否",
  "url": "https://towardsdatascience.com/practical-nlp-summarising-short-and-long-speeches-with-hugging-faces-pipeline-bc7df76bd366/",
  "publish_date": "2020-07-27",
  "corpus_stage": "",
  "notes": "",
  "id": "d7f4b19b8b0f",
  "collected_at": "2026-10-02T09:41:44",
  "title": "Practical NLP: Summarising Short and Long Speeches With Hugging Face's Pipeline | Towards Data Science",
  "body_markdown": "![Revisit consent button](../images/body-df29beacd7496220cc94.svg)\nWe value your privacy\nWe use cookies to enhance your browsing experience, serve personalised ads or content, and analyse our traffic. By clicking \"Accept All\", you consent to our use of cookies.\nCustomise Reject All Accept All\nCustomise Consent Preferences ![](../images/body-2159bb67f3de8ad870eb.svg)\nWe use cookies to help you navigate efficiently and perform certain functions. You will find detailed information about all cookies under each consent category below.\nThe cookies that are categorised as \"Necessary\" are stored on your browser as they are essential for enabling the basic functionalities of the site. ... Show more\nNecessaryAlways Active\nNecessary cookies are required to enable the basic features of this site, such as providing secure log-in or adjusting your consent preferences. These cookies do not store any personally identifiable data.\n  * Cookie\nBCTempID\n  * Duration\n10 minutes\n  * Description\nNo description available.\n\n\n  * Cookie\n__cf_bm\n  * Duration\n1 hour\n  * Description\nThis cookie, set by Cloudflare, is used to support Cloudflare Bot Management. \n\n\n  * Cookie\nAWSALBCORS\n  * Duration\n7 days\n  * Description\nAmazon Web Services set this cookie for load balancing.\n\n\n  * Cookie\n_cfuvid\n  * Duration\nsession\n  * Description\nCloudflare sets this cookie to track users across sessions to optimize user experience by maintaining session consistency and providing personalized services\n\n\n  * Cookie\nli_gc\n  * Duration\n6 months\n  * Description\nLinkedin set this cookie for storing visitor's consent regarding using cookies for non-essential purposes.\n\n\n  * Cookie\n__hssrc\n  * Duration\nsession\n  * Description\nThis cookie is set by Hubspot whenever it changes the session cookie. The __hssrc cookie set to 1 indicates that the user has restarted the browser, and if the cookie does not exist, it is assumed to be a new session.\n\n\n  * Cookie\n__hssc\n  * Duration\n1 hour\n  * Description\nHubSpot sets this cookie to keep track of sessions and to determine if HubSpot should increment the session number and timestamps in the __hstc cookie.\n\n\n  * Cookie\nwpEmojiSettingsSupports\n  * Duration\nsession\n  * Description\nWordPress sets this cookie when a user interacts with emojis on a WordPress site. It helps determine if the user's browser can display emojis properly.\n\n\n  * Cookie\nBCSessionID\n  * Duration\n1 year 1 month 4 days\n  * Description\nBlueconic sets this cookie as a unique identifier for the BlueConic profile.\n\n\n  * Cookie\n_octo\n  * Duration\n1 year\n  * Description\nNo description available.\n\n\n  * Cookie\nlogged_in\n  * Duration\n1 year\n  * Description\nNo description available.\n\n\n  * Cookie\n__Secure-YEC\n  * Duration\npast\n  * Description\nYouTube sets this cookie to stores the user's video player preferences using embedded YouTube video\n\n\n  * Cookie\n__eoi\n  * Duration\n6 months\n  * Description\nDescription is currently not available.\n\n\n  * Cookie\nAWSALBTGCORS\n  * Duration\n7 days\n  * Description\nNo description available.\n\n\n  * Cookie\nlogin-status-p\n  * Duration\npast\n  * Description\nDescription is currently not available.\n\n\n  * Cookie\nAWSALBTG\n  * Duration\n7 days\n  * Description\nNo description available.\n\n\n  * Cookie\ncsrf_token\n  * Duration\nsession\n  * Description\nNo description available.\n\n\n  * Cookie\ntoken_v2\n  * Duration\n1 day\n  * Description\nDescription is currently not available.\n\n\n  * Cookie\nD\n  * Duration\n1 year\n  * Description\nDescription is currently not available.\n\n\n  * Cookie\nPHPSESSID\n  * Duration\nsession\n  * Description\nThis cookie is native to PHP applications. The cookie stores and identifies a user's unique session ID to manage user sessions on the website. The cookie is a session cookie and will be deleted when all the browser windows are closed.\n\n\n  * Cookie\nVISITOR_PRIVACY_METADATA\n  * Duration\n6 months\n  * Description\nYouTube sets this cookie to store the user's cookie consent state for the current domain. \n\n\n  * Cookie\ncookietest\n  * Duration\nsession\n  * Description\nThe cookietest cookie is typically used to determine whether the user's browser accepts cookies, essential for website functionality and user experience.\n\n\n  * Cookie\n__Host-airtable-session\n  * Duration\n1 year\n  * Description\nThis cookie is used to enable us to integrate the services of Airtable.\n\n\n  * Cookie\n__Host-airtable-session.sig\n  * Duration\n1 year\n  * Description\nThis cookie is used to enable us to integrate the services of Airtable.\n\n\n  * Cookie\nm\n  * Duration\n1 year 1 month 4 days\n  * Description\nStripe sets this cookie for fraud prevention purposes. It identifies the device used to access the website, allowing the website to be formatted accordingly.\n\n\n  * Cookie\nBIGipServer*\n  * Duration\nsession\n  * Description\nMarketo sets this cookie to collect information about the user's online activity and build a profile about their interests to provide advertisements relevant to the user. \n\n\n  * Cookie\n__cfruid\n  * Duration\nsession\n  * Description\nCloudflare sets this cookie to identify trusted web traffic.\n\n\n  * Cookie\n_GRECAPTCHA\n  * Duration\n6 months\n  * Description\nGoogle Recaptcha service sets this cookie to identify bots to protect the website against malicious spam attacks.\n\n\n  * Cookie\n__Secure-YNID\n  * Duration\n6 months\n  * Description\nGoogle cookie used to protect user security and prevent fraud, especially during the login process.\n\n\n  * Cookie\ncookieyes-consent\n  * Duration\n1 year\n  * Description\nCookieYes sets this cookie to remember users' consent preferences so that their preferences are respected on subsequent visits to this site. It does not collect or store any personal information about the site visitors.\n\n\nFunctional\nFunctional cookies help perform certain functionalities like sharing the content of the website on social media platforms, collecting feedback, and other third-party features.\n  * Cookie\nlidc\n  * Duration\n1 day\n  * Description\nLinkedIn sets the lidc cookie to facilitate data center selection.\n\n\n  * Cookie\nbrw\n  * Duration\n1 year\n  * Description\nNo description available.\n\n\n  * Cookie\nbrwConsent\n  * Duration\n5 minutes\n  * Description\nDescription is currently not available.\n\n\n  * Cookie\nWMF-Uniq\n  * Duration\n1 year\n  * Description\nDescription is currently not available.\n\n\n  * Cookie\nloom_anon_comment\n  * Duration\n1 year\n  * Description\nNo description available.\n\n\n  * Cookie\nloom_referral_video\n  * Duration\nsession\n  * Description\nDescription is currently not available.\n\n\n  * Cookie\nVISITOR_INFO1_LIVE\n  * Duration\n6 months\n  * Description\nA cookie set by YouTube to measure bandwidth that determines whether the user gets the new or old player interface.\n\n\n  * Cookie\nyt-remote-connected-devices\n  * Duration\nNever Expires\n  * Description\nYouTube sets this cookie to store the user's video preferences using embedded YouTube videos.\n\n\n  * Cookie\nytidb::LAST_RESULT_ENTRY_KEY\n  * Duration\nNever Expires\n  * Description\nThe cookie ytidb::LAST_RESULT_ENTRY_KEY is used by YouTube to store the last search result entry that was clicked by the user. This information is used to improve the user experience by providing more relevant search results in the future.\n\n\n  * Cookie\nyt-remote-device-id\n  * Duration\nNever Expires\n  * Description\nYouTube sets this cookie to store the user's video preferences using embedded YouTube videos.\n\n\n  * Cookie\nyt-remote-session-name\n  * Duration\nsession\n  * Description\nThe yt-remote-session-name cookie is used by YouTube to store the user's video player preferences using embedded YouTube video.\n\n\n  * Cookie\nyt-remote-fast-check-period\n  * Duration\nsession\n  * Description\nThe yt-remote-fast-check-period cookie is used by YouTube to store the user's video player preferences for embedded YouTube videos.\n\n\n  * Cookie\nyt-remote-session-app\n  * Duration\nsession\n  * Description\nThe yt-remote-session-app cookie is used by YouTube to store user preferences and information about the interface of the embedded YouTube video player.\n\n\n  * Cookie\nyt-remote-cast-available\n  * Duration\nsession\n  * Description\nThe yt-remote-cast-available cookie is used to store the user's preferences regarding whether casting is available on their YouTube video player.\n\n\n  * Cookie\nyt-remote-cast-installed\n  * Duration\nsession\n  * Description\nThe yt-remote-cast-installed cookie is used to store the user's video player preferences using embedded YouTube video.\n\n\n  * Cookie\ncp_session\n  * Duration\n3 months\n  * Description\nCodepen sets this cookie for Help systems found in the website.\n\n\n  * Cookie\nloid\n  * Duration\n1 year 1 month 4 days\n  * Description\nThis cookie is set by the Reddit. The cookie enables the sharing of content from the website onto the social media platform.\n\n\nAnalytics\nAnalytical cookies are used to understand how visitors interact with the website. These cookies help provide information on metrics such as the number of visitors, bounce rate, traffic source, etc.\n  * Cookie\n__hstc\n  * Duration\n6 months\n  * Description\nHubspot set this main cookie for tracking visitors. It contains the domain, initial timestamp (first visit), last timestamp (last visit), current timestamp (this visit), and session number (increments for each subsequent session).\n\n\n  * Cookie\nhubspotutk\n  * Duration\n6 months\n  * Description\nHubSpot sets this cookie to keep track of the visitors to the website. This cookie is passed to HubSpot on form submission and used when deduplicating contacts.\n\n\n  * Cookie\n_ga\n  * Duration\n1 year 1 month 4 days\n  * Description\nGoogle Analytics sets this cookie to calculate visitor, session and campaign data and track site usage for the site's analytics report. The cookie stores information anonymously and assigns a randomly generated number to recognise unique visitors.\n\n\n  * Cookie\n_ga_*\n  * Duration\n1 year 1 month 4 days\n  * Description\nGoogle Analytics sets this cookie to store and count page views.\n\n\n  * Cookie\n__Host-psifi.analyticsTrace\n  * Duration\n6 hours\n  * Description\nDescription is currently not available.\n\n\n  * Cookie\n__Host-psifi.analyticsTraceV2\n  * Duration\n6 hours\n  * Description\nDescription is currently not available.\n\n\n  * Cookie\n_gh_sess\n  * Duration\nsession\n  * Description\nGitHub sets this cookie for temporary application and framework state between pages like what step the user is on in a multiple step form.\n\n\n  * Cookie\nYSC\n  * Duration\nsession\n  * Description\nYSC cookie is set by Youtube and is used to track the views of embedded videos on Youtube pages.\n\n\n  * Cookie\najs_anonymous_id\n  * Duration\n1 year\n  * Description\nThis cookie is set by Segment to count the number of people who visit a certain site by tracking if they have visited before.\n\n\n  * Cookie\nvuid\n  * Duration\n1 year 1 month 4 days\n  * Description\nVimeo installs this cookie to collect tracking information by setting a unique ID to embed videos on the website. \n\n\nPerformance\nPerformance cookies are used to understand and analyse the key performance indexes of the website which helps in delivering a better user experience for the visitors.\n  * Cookie\nAWSALB\n  * Duration\n7 days\n  * Description\nAWSALB is an application load balancer cookie set by Amazon Web Services to map the session to the target.\n\n\n  * Cookie\nacq\n  * Duration\npast\n  * Description\nDescription is currently not available.\n\n\n  * Cookie\nacq.sig\n  * Duration\npast\n  * Description\nDescription is currently not available.\n\n\n  * Cookie\nptc\n  * Duration\n2 years\n  * Description\nNo description available.\n\n\nAdvertisement\nAdvertisement cookies are used to provide visitors with customised advertisements based on the pages you visited previously and to analyse the effectiveness of the ad campaigns.\n  * Cookie\nmuc_ads\n  * Duration\n1 year 1 month 4 days\n  * Description\nTwitter sets this cookie to collect user behaviour and interaction data to optimize the website.\n\n\n  * Cookie\nguest_id_marketing\n  * Duration\n1 year 1 month 4 days\n  * Description\nTwitter sets this cookie to identify and track the website visitor.\n\n\n  * Cookie\nguest_id_ads\n  * Duration\n1 year 1 month 4 days\n  * Description\nTwitter sets this cookie to identify and track the website visitor.\n\n\n  * Cookie\npersonalization_id\n  * Duration\n1 year 1 month 4 days\n  * Description\nTwitter sets this cookie to integrate and share features for social media and also store information about how the user uses the website, for tracking and targeting.\n\n\n  * Cookie\nguest_id\n  * Duration\n1 year 1 month 4 days\n  * Description\nTwitter sets this cookie to identify and track the website visitor. It registers if a user is signed in to the Twitter platform and collects information about ad preferences.\n\n\n  * Cookie\nbcookie\n  * Duration\n1 year\n  * Description\nLinkedIn sets this cookie from LinkedIn share buttons and ad tags to recognize browser IDs.\n\n\n  * Cookie\n__Secure-ROLLOUT_TOKEN\n  * Duration\n6 months\n  * Description\nYouTube sets this cookie to manage feature rollout and experimentation. It helps Google control which new features or interface changes are shown to users as part of testing and staged rollouts, ensuring consistent experience for a given user during an experiment.\n\n\n  * Cookie\nyt.innertube::nextId\n  * Duration\nNever Expires\n  * Description\nYouTube sets this cookie to register a unique ID to store data on what videos from YouTube the user has seen.\n\n\n  * Cookie\nyt.innertube::requests\n  * Duration\nNever Expires\n  * Description\nYouTube sets this cookie to register a unique ID to store data on what videos from YouTube the user has seen.\n\n\n  * Cookie\nsession_tracker\n  * Duration\nsession\n  * Description\nThis cookie is set by the Reddit. This cookie is used to identify trusted web traffic. It also helps in adverstising on the website.\n\n\n  * Cookie\nedgebucket\n  * Duration\nsession\n  * Description\nReddit sets this cookie to save the information about a log-on Reddit user, for the purpose of advertisement recommendations and updating the content.\n\n\n  * Cookie\ndid\n  * Duration\n1 year\n  * Description\nArbor sets this cookie to show targeted ads to site visitors.This cookie expires after 2 months or 1 year.\n\n\nUncategorised\nOther uncategorised cookies are those that are being analysed and have not been classified into a category as yet.\nNo cookies to display.\nReject All Save My Preferences Accept All\nToggle Search[](https://towardsdatascience.com/)Toggle Mobile Navigation\n[](https://towardsdatascience.com/)\nPublish AI, ML & data-science insights to a global community of data professionals.\nSign in[Submit an Article](https://write.insightmediagroup.io)\n[Latest](https://towardsdatascience.com/latest/)[Editor's Picks](https://towardsdatascience.com/tag/editors-pick/)[Newsletter](https://towardsdatascience.com/tag/the-variable/)[ShipAI](https://towardsdatascience.com/shipai/)\n* * *\n[Write For TDS](https://towardsdatascience.com/submissions/)\n[LinkedIn](https://www.linkedin.com/company/towards-data-science/)[X](https://x.com/TDataScience)Toggle Search\n[Data Science](https://towardsdatascience.com/category/data-science/)\n# Practical NLP: Summarising Short and Long Speeches With Hugging Face's Pipeline\nCompared to sentiment analysis or classification, text summarisation is a far less ubiquitous NLP task due to the time and resources...\n[Chua Chin Hon](https://towardsdatascience.com/author/chuachinhon/)\nJuly 27, 20206 min read\n### _Compared to sentiment analysis or classification, text summarisation is a far less ubiquitous NLP task due to the time and resources needed to execute it well. Hugging Face's transformers pipeline has changed that. Here's a quick demo of how you can summarise short and long speeches easily._\n![Screen grabs from PAP.org.sg \\(left\\) and WP.sg \\(right\\).](../images/000.jpeg)\nScreen grabs from [PAP.org.sg](https://www.pap.org.sg/) (left) and [WP.sg](https://www.wp.sg/message-for-voters/) (right).\nSummarising a speech is more art than science, some might argue. But recent advances in NLP could well test the validity of that argument.\nIn particular, Hugging Face's (HF) transformers [summarisation pipeline](https://huggingface.co/transformers/_modules/transformers/pipelines.html#SummarizationPipeline) has made the task easier, faster and more efficient to execute. Admittedly, there's still a hit-and-miss quality to current results. But there are also flashes of brilliance that hint at the possibilities to come as language models become more sophisticated.\nThis post will demonstrate how you can easily use HF's pipeline to summarise both short and long speeches. A minor work-around is needed for long speeches due to the maximum sequence limit for models used in the pipeline. I've also included the code for a simple text summarisation web app that you can run on your local machine.\n* * *\nLearn this step by step with the interactive [AI and Data Scientist](https://roadmap.sh/ai-data-scientist), [Machine Learning](https://roadmap.sh/machine-learning) and [Computer Science](https://roadmap.sh/computer-science) roadmaps.\n## 1. DATA, NOTEBOOK AND CAVEATS\nHere's the [Github repo](https://github.com/chuachinhon/practical_nlp) for this post. The [toy dataset used in this post](https://github.com/chuachinhon/practical_nlp/blob/master/data/ge2020_speeches.csv) is based on a small collection of political speeches/statements made during Singapore's General Election in July 2020. The word-count for the speeches, by politicians from the ruling People's Action Party (PAP) as well as the Opposition, range from 236 words to 3,746 words.\n![](../images/001.jpeg)\nThe simple workflow outlined in my [notebook ](https://github.com/chuachinhon/practical_nlp/blob/master/notebooks/2.0_speech_summary_cch.ipynb)should work for any other collection of speeches you care to put together in a CSV file. The HF summarisation pipeline doesn't work for non-English speeches as far as I know.\nI'm using the pipeline out of the box, meaning the results stem from the default [bart-large-cnn](https://huggingface.co/facebook/bart-large-cnn) model. Feel free to test with other [models tuned for this task](https://huggingface.co/models?filter=pipeline:summarization).\n## 2. SUMMARISING SHORT SPEECHES\nIt is unlikely that anyone needs to batch run summaries of dozens or hundreds of short speeches in one go. But in the off-chance that you do, the HF pipeline's ease of use is a lifesaver.\nHere, I ran the pipeline on seven speeches under 800 words. Results and time taken may vary according to the computer and version of the transformers library you are using.\nThis was run on a 2018 Mac Mini with 64Gb of RAM on version 2.11.0 of the [transformers library](https://pypi.org/project/transformers/). You can download the results [here](https://www.dropbox.com/s/bgdi2p7vs2d8igy/short_results.csv) and compare the summaries with the original text:\n![Download this CSV file here.](../images/002.jpeg)\nDownload this CSV file [here](https://www.dropbox.com/s/bgdi2p7vs2d8igy/short_results.csv).\nSome of the summaries are so-so. There's a clear tendency for the model to use the first and second sentences in a speech. This could work well for summaries of news stories, but less so for those involving political/corporate speeches.\nBut the model turned up one brilliant summary, the fifth one w[here](https://www.wp.sg/message-for-voters/) Workers' Party (WP) leader Pritam Singh laid out his party's main pitch to voters. See the original statement here, and here's a side-by-side comparison with the summary by the Bart model.\n![Image on left: Screen grab from WP.sg.](../images/003.jpeg)\nImage on left: Screen grab from [WP.sg](https://www.wp.sg/message-for-voters/).\nIn just 59 words, the Bart model perfectly distilled the essence of the WP's campaign message from a 627-word statement. Those who are familiar with Singapore politics would appreciate how sharp this summary is, particularly given that the model was not tuned on data involving Singapore news or politics.\nIt is not unreasonable to ask whether this summary is a fluke or happy coincidence. I ran the same set of speeches through the latest version of transformers (version 3.0.2) and got a less impressive translation: \"The workers party offers singaporeans a choice and an alternative voice in parliament . The wp has contributed significantly to the democratic processes in our country . Former pap mps who wrote their memoirs after have helped to expose the limitations of a parliament dominated by the pap\".\nConsistency of quality appears to be a problem at this point. A fuller test, unfortunately, is outside the scope of this post.\n## 3. SUMMARISING A LONG SPEECH\nThis is likely the use case for most people: Summarising a long speech running into thousands of words. To get around the sequence length limits in the pipeline/models, I used a function to break up the text into a number of chunks of fixed-character length.\nTo keep this post concise, I decided to run the HF pipeline on the longest speech - 3,746 words - in the toy dataset. I broke up the speech into 9 chunks of 2,500 characters each.\nThe function allows you to easily set the character length that you prefer. This approach is admittedly clumsy, but I find it useful for a closer inspection of the results. Details are in my [notebook](https://github.com/chuachinhon/practical_nlp/blob/master/notebooks/2.0_speech_summary_cch.ipynb).\nHere's the [link to the original speech](https://www.pap.org.sg/news/ge2020-news/ge2020-lunch-time-rally/), and a CSV file comparing the original text with the mini-summaries can be dowloaded [here](<http://A> CSV file comparing the original text with the mini-summaries can be dowloaded here). This is a side-by-side comparison of how the model summarised it :\n![Image on left: Screen grab from Pap.org.sg. Download the CSV results file of the long-speech summarisation here.](../images/004.jpeg)\nImage on left: Screen grab from [Pap.org.sg](https://www.pap.org.sg/). Download the CSV results file of the long-speech summarisation [here](https://www.dropbox.com/s/uqs14olj69mzntq/long_speech_results.csv).\nThe 536-word \"combined summary\" is not as brilliant as the WP example I highlighted above, but it's pretty decent (except for the section highlighted in red, which I'll discuss in a bit) for a first draft. If I'm in a crunch, this is something I can quickly edit into a more useable form.\nSingaporean users will easily pick out an obvious mistake in the summary of the sixth section: \"Pap Lee Kuan Yew seeks not just your mandate but your strong mandate to lead singapore through this crisis.\"\nThe original paragraph reads: \"Investors will scrutinise the election results, and act on their conclusions. So will others, both friends and adversaries of Singapore. That is why in this election, the PAP seeks not just your mandate, but your strong mandate, to lead Singapore through this crisis.\"\nLee Kuan Yew, Singapore's founding prime minister, [died in March 2015](https://en.wikipedia.org/wiki/Lee_Kuan_Yew). However, those with more than a passing knowledge of Singapore politics might flippantly argue that the model was in fact correct and not erroneous with the above summary mentioning Lee Kuan Yew. But that is a discussion for another day ;p.\n## 4. WEB APP\nI tried to deploy a simple web app to let non-notebook users try the summarisation pipeline, but discovered that [free accounts on Pythonanywhere.com don't have enough storage space](https://www.pythonanywhere.com/forums/topic/26685/) for the libraries required, particularly pytorch.\nSo instead I uploaded a [version that runs on local machines](https://github.com/chuachinhon/practical_nlp/blob/master/apps/summary_app_local.py), if you are keen to let colleagues at the workplace try it out. The code for the web app was adapted from two sources: the [main one](https://github.com/Csmith715/Summarizer) from Github and a [second one by Plotly](https://github.com/plotly/dash-sample-apps/blob/master/apps/dash-summarize/app.py) using DistilBart.\n![](../images/body-53dbc2978e9a4dd3a787.gif)\nIf the app returns an error, it means the text you've copied has exceeded the maximum number of tokens. Try summarising in smaller chunks or use the function in the notebook to break up the text. I'll try to deploy the app online in future if Pythonanywhere.com includes pytorch as part of their pre-installed packages.\n## 5. CONCLUSION\nWith more language models being added by the week, there's ample scope for experimentation and more robust tests of the HF pipeline's summarisation output. My own experience so far is that using a different model doesn't necessarily translate into better results.\nTowards the end of my notebook, I switched to the T[5 mode](https://huggingface.co/transformers/model_doc/t5.html)l for a trial but found that the summarisations for three short speeches weren't better than those churned out by the default Bart model.\nBut we are certainly getting a good glimpse of the future possibilities as companies like Hugging Face and OpenAI continue to push the NLP envelope. I'm certainly curious to see how GPT-3 performs on this task, given the [impressive results](https://gpt3examples.com/) it has achieved on a diverse range of tasks so far.\nMeanwhile, those who want to experiment further by fine tuning a transformer model with their own dataset can check out these two notebooks [[here](https://github.com/ohmeow/ohmeow_website/blob/master/_notebooks/2020-05-23-text-generation-with-blurr.ipynb)](https://github.com/abhimishra91/transformers-tutorials/blob/master/transformers_summarization_wandb.ipynb) and here. Finding a good dataset for your use case, however, could be more challenging than you think.\nIf you spot errors in this post or the notebook, ping me at:\nTwitter: [Chua Chin Hon](https://medium.com/u/b3d8090c0aee?source=post_page-----aad7f2e1d0a0----------------------)\nLinkedIn: [www.linkedin.com/in/chuachinhon](http://www.linkedin.com/in/chuachinhon)\n## Register Now to Continue Reading\n### Unlock Towards Data Science with a Free Account\nAll articles on Towards Data Science are available to registered users. Create a free account today to access this article and thousands more.\nRegister for freeSign in to your account\n* * *\n![](../images/body-d56b810b4697a0fbf983.png)\nWritten By\n## [Chua Chin Hon](https://towardsdatascience.com/author/chuachinhon/)\n[See all from Chua Chin Hon](https://towardsdatascience.com/author/chuachinhon/)\n[Data Science](https://towardsdatascience.com/tag/data-science/)[Hugging Face](https://towardsdatascience.com/tag/hugging-face/)[NLP](https://towardsdatascience.com/tag/nlp/)[Science](https://towardsdatascience.com/tag/science/)[Singapore](https://towardsdatascience.com/tag/singapore/)\nShare This Article\n  * [](https://www.facebook.com/sharer/sharer.php?u=https%3A%2F%2Ftowardsdatascience.com%2Fpractical-nlp-summarising-short-and-long-speeches-with-hugging-faces-pipeline-bc7df76bd366%2F&title=Practical%20NLP%3A%20Summarising%20Short%20and%20Long%20Speeches%20With%20Hugging%20Face's%20Pipeline)\n  * [](https://www.linkedin.com/shareArticle?mini=true&url=https%3A%2F%2Ftowardsdatascience.com%2Fpractical-nlp-summarising-short-and-long-speeches-with-hugging-faces-pipeline-bc7df76bd366%2F&title=Practical%20NLP%3A%20Summarising%20Short%20and%20Long%20Speeches%20With%20Hugging%20Face's%20Pipeline)\n  * [](https://x.com/share?url=https%3A%2F%2Ftowardsdatascience.com%2Fpractical-nlp-summarising-short-and-long-speeches-with-hugging-faces-pipeline-bc7df76bd366%2F&text=Practical%20NLP%3A%20Summarising%20Short%20and%20Long%20Speeches%20With%20Hugging%20Face's%20Pipeline)\n\n\nTowards Data Science is a community publication. Submit your insights to reach our global audience and earn through the TDS Author Payment Program.\n[Write for TDS](https://towardsdatascience.com/submissions/)\n## Related Articles\n  * [![](../images/005.jpeg)](https://towardsdatascience.com/exploratory-data-analysis-with-python-part-2-9b0b6c1402c2/)\n## [Exploratory Data Analysis with Python - Part 2](https://towardsdatascience.com/exploratory-data-analysis-with-python-part-2-9b0b6c1402c2/)\n[Data Science](https://towardsdatascience.com/category/data-science/)\nA template to follow to get you started analyzing data with Python and Pandas.\n[Gustavo Santos](https://towardsdatascience.com/author/gustavorsantos/)\nNovember 29, 20211 min read\n  * [![](../images/006.png)](https://towardsdatascience.com/downscaling-a-satellite-thermal-image-from-1000-m-to-10-m-python-3b2ed19ff103/)\n## [Downscaling a Satellite Thermal Image from 1000 m to 10 m (Python)](https://towardsdatascience.com/downscaling-a-satellite-thermal-image-from-1000-m-to-10-m-python-3b2ed19ff103/)\n[Data Science](https://towardsdatascience.com/category/data-science/)\nThermal Sharpening of Sentinel-3 Images: From 1 Km to 10 m Using Python in Google Colab\n[Mahyar Aboutalebi](https://towardsdatascience.com/author/mahyar.aboutalebi/)\nMarch 6, 202413 min read\n  * [![](../images/007.jpg)](https://towardsdatascience.com/sql-servers-secret-feature-run-python-and-add-ons-natively-in-sql-server-7f3c4efe5c00/)\n## [SQL Server's Secret Feature - Run Python and Add-Ons Natively In SQL Server.](https://towardsdatascience.com/sql-servers-secret-feature-run-python-and-add-ons-natively-in-sql-server-7f3c4efe5c00/)\n[Machine Learning](https://towardsdatascience.com/category/artificial-intelligence/machine-learning/)\nImport Python libraries, manipulate and output SQL tables and more, all without leaving SQL server.\n[Sasha Korovkina](https://towardsdatascience.com/author/sasha.korovkina2003/)\nMay 15, 20244 min read\n  * [![](../images/008.jpeg)](https://towardsdatascience.com/python-pandas-gives-you-full-control-over-timestamps-10fe26c96a9e/)\n## [Python Pandas Gives You Full Control Over Timestamps](https://towardsdatascience.com/python-pandas-gives-you-full-control-over-timestamps-10fe26c96a9e/)\n[Artificial Intelligence](https://towardsdatascience.com/category/artificial-intelligence/)\nFrom years to nanoseconds.\n[Soner Yıldırım](https://towardsdatascience.com/author/sonery/)\nJanuary 15, 20226 min read\n  * [![](../images/009.jpeg)](https://towardsdatascience.com/five-rules-for-designing-tables-a953a16e50f3/)\n## [Five Rules for Designing Tables](https://towardsdatascience.com/five-rules-for-designing-tables-a953a16e50f3/)\n[Data Science](https://towardsdatascience.com/category/data-science/)\nSimple design elements to make your spreadsheets pop\n[Alana Pirrone](https://towardsdatascience.com/author/alanapirrone/)\nJuly 27, 20214 min read\n  * [![](../images/010.gif)](https://towardsdatascience.com/how-to-optimize-your-switchback-a-b-test-configuration-791a28bee678/)\n## [How to Optimize your Switchback A/B Test Configuration](https://towardsdatascience.com/how-to-optimize-your-switchback-a-b-test-configuration-791a28bee678/)\n[Data Science](https://towardsdatascience.com/category/data-science/)\nAn algorithm that determines the most effective randomization points for a switchback experiment.\n[Michael Berk](https://towardsdatascience.com/author/michaelberk/)\nJune 8, 20219 min read\n  * [![](../images/011.jpg)](https://towardsdatascience.com/simple-explanations-of-basic-statistics-concepts-part-1-bba66a1761d7/)\n## [Simple Explanations of Basic Statistics Concepts (Part 1)](https://towardsdatascience.com/simple-explanations-of-basic-statistics-concepts-part-1-bba66a1761d7/)\n[Data Science](https://towardsdatascience.com/category/data-science/)\nSimple and comprehensive explanations of different statistical concepts\n[Chi Nguyen](https://towardsdatascience.com/author/nphchi223/)\nJune 7, 20227 min read\n  * [![](../images/012.jpg)](https://towardsdatascience.com/a-guide-to-using-h2o-ai-in-r-99cf6265bc05/)\n## [A Guide to using h2o.ai in R](https://towardsdatascience.com/a-guide-to-using-h2o-ai-in-r-99cf6265bc05/)\n[Machine Learning](https://towardsdatascience.com/category/artificial-intelligence/machine-learning/)\nLearn how to work with the h2o library using the R Language\n[Ivo Bernardo](https://towardsdatascience.com/author/ivopbernardo/)\nNovember 2, 202215 min read\n  * [![](../images/013.jpg)](https://towardsdatascience.com/why-you-shouldnt-hire-more-data-scientists-3188a1597fa3/)\n## [Why You Shouldn't Hire More Data Scientists](https://towardsdatascience.com/why-you-shouldnt-hire-more-data-scientists-3188a1597fa3/)\n[Data Science](https://towardsdatascience.com/category/data-science/)\nSave money and frustration by considering the roles of analysts, data engineers, and software engineers...\n[Adam Sroka](https://towardsdatascience.com/author/aesroka/)\nApril 26, 20217 min read\n\n\n  * [YouTube](https://www.youtube.com/c/TowardsDataScience)\n  * [X](https://x.com/TDataScience)\n  * [LinkedIn](https://www.linkedin.com/company/towards-data-science/)\n  * [Threads](https://www.threads.net/@towardsdatascience)\n  * [Bluesky](https://bsky.app/profile/towardsdatascience.com)\n\n\n[](https://towardsdatascience.com/)\nYour home for data science and AI. The world's leading publication for data science, data analytics, data engineering, machine learning, and artificial intelligence professionals.\nSubscribe to Our Newsletter[Write For TDS](https://towardsdatascience.com/submissions/)•[About](https://towardsdatascience.com/about-towards-data-science-d691af11cc2f/)•[Advertise](https://contact.towardsdatascience.com/advertise-with-towards-data-science)•[Privacy Policy](https://towardsdatascience.com/privacy-policy/)•[Terms of Use](https://towardsdatascience.com/website-terms-of-use/)\n© Insight Media Group, LLC 2026•[The New Stack](https://thenewstack.io)•[roadmap.sh](https://roadmap.sh)•[Creator Network](https://insightmediagroup.io/creator-network)\n####  Recommended Articles \nClose\n  * [ ![](https://h030.towardsdatascience.com/rest/v2/contentStores/d7d3a4ab-7caf-4383-bbd2-fbf7fb4f1787/items/607639/image?etag=1781250125848&width=500&height=500) Javascript Fatigue: HTMX Is All You Need to Build ChatGPT — Part 1 ](https://towardsdatascience.com/javascript-fatigue-you-dont-need-js-to-build-chatgpt/ \"Javascript Fatigue: HTMX Is All You Need to Build ChatGPT — Part 1\")\n  * [ ![](https://h030.towardsdatascience.com/rest/v2/contentStores/d7d3a4ab-7caf-4383-bbd2-fbf7fb4f1787/items/607930/image?etag=1782124566054&width=500&height=500) The Subset Sum Problem Solved in Linear Time for Dense Enough Inputs ](https://towardsdatascience.com/subset-sum-problem-solved-in-linear-time-for-dense-enough-inputs/ \"The Subset Sum Problem Solved in Linear Time for Dense Enough Inputs\")\n  * [ ![](https://h030.towardsdatascience.com/rest/v2/contentStores/d7d3a4ab-7caf-4383-bbd2-fbf7fb4f1787/items/3660/image?etag=1780176246070&width=500&height=500) Does Your Company Have a Data Strategy? ](https://towardsdatascience.com/does-your-company-have-a-data-strategy-e8944dbec4ae/ \"Does Your Company Have a Data Strategy?\")\n  * [ ![](https://h030.towardsdatascience.com/rest/v2/contentStores/d7d3a4ab-7caf-4383-bbd2-fbf7fb4f1787/items/4970/image?etag=1780565225463&width=500&height=500) Productivity Tips, Data Career Insights, and Other Recent Must-Reads ](https://towardsdatascience.com/productivity-tips-data-career-insights-and-other-recent-must-reads-5c47eb7a2fe0/ \"Productivity Tips, Data Career Insights, and Other Recent Must-Reads\")\n\n\n",
  "metadata": {
    "title": "Practical NLP: Summarising Short and Long Speeches With Hugging Face's Pipeline | Towards Data Science",
    "description": "Compared to sentiment analysis or classification, text summarisation is a far less ubiquitous NLP task due to the time and resources...",
    "keywords": null,
    "author": "Chua Chin Hon",
    "og:locale": "en_US",
    "og:type": "article",
    "og:title": "Practical NLP: Summarising Short and Long Speeches With Hugging Face's Pipeline",
    "og:description": "Compared to sentiment analysis or classification, text summarisation is a far less ubiquitous NLP task due to the time and resources...",
    "og:url": "https://towardsdatascience.com/practical-nlp-summarising-short-and-long-speeches-with-hugging-faces-pipeline-bc7df76bd366/",
    "og:site_name": "Towards Data Science",
    "og:image": "https://assets.insightmediagroup.io/media/wp-content/uploads/2020/07/1KMA8dQRCoIh_nEJsgYUkQw.jpeg",
    "og:image:width": "2087",
    "og:image:height": "593",
    "og:image:type": "image/jpeg",
    "twitter:card": "summary_large_image",
    "twitter:site": "@TDataScience",
    "twitter:creator": "@TDataScience",
    "twitter:label1": "Written by",
    "twitter:data1": "Chua Chin Hon",
    "twitter:label2": "Est. reading time",
    "twitter:data2": "6 min read",
    "article:published_time": "2020-07-27T06:09:59.000Z",
    "article:modified_time": "2020-07-27T06:09:59.000Z",
    "article:author": "https://towardsdatascience.com/author/chuachinhon/"
  },
  "paywall": {
    "listed_paid": false,
    "detected": false,
    "marker": "",
    "public_only": false,
    "policy": "付费/会员内容只保留页面公开可见部分，不绕过付费墙"
  },
  "crawl": {
    "success": true,
    "status_code": 200,
    "error": ""
  },
  "code_blocks": [],
  "images": [
    {
      "status": "http_404",
      "url": "https://h030.towardsdatascience.com/rest/v2/contentStores/d7d3a4ab-7caf-4383-bbd2-fbf7fb4f1787/items/607639/image?etag=1781250125848&width=500&height=500",
      "src": "https://h030.towardsdatascience.com/rest/v2/contentStores/d7d3a4ab-7caf-4383-bbd2-fbf7fb4f1787/items/607639/image?etag=1781250125848&width=500&height=500",
      "alt": "",
      "downloaded": false,
      "discovery": "saved_markdown",
      "retried_at": "2026-10-20T17:49:33Z"
    },
    {
      "status": "http_404",
      "url": "https://h030.towardsdatascience.com/rest/v2/contentStores/d7d3a4ab-7caf-4383-bbd2-fbf7fb4f1787/items/607930/image?etag=1782124566054&width=500&height=500",
      "src": "https://h030.towardsdatascience.com/rest/v2/contentStores/d7d3a4ab-7caf-4383-bbd2-fbf7fb4f1787/items/607930/image?etag=1782124566054&width=500&height=500",
      "alt": "",
      "downloaded": false,
      "discovery": "saved_markdown",
      "retried_at": "2026-10-20T17:49:33Z"
    },
    {
      "status": "http_404",
      "url": "https://h030.towardsdatascience.com/rest/v2/contentStores/d7d3a4ab-7caf-4383-bbd2-fbf7fb4f1787/items/3660/image?etag=1780176246070&width=500&height=500",
      "src": "https://h030.towardsdatascience.com/rest/v2/contentStores/d7d3a4ab-7caf-4383-bbd2-fbf7fb4f1787/items/3660/image?etag=1780176246070&width=500&height=500",
      "alt": "",
      "downloaded": false,
      "discovery": "saved_markdown",
      "retried_at": "2026-10-20T17:49:33Z"
    },
    {
      "status": "http_404",
      "url": "https://h030.towardsdatascience.com/rest/v2/contentStores/d7d3a4ab-7caf-4383-bbd2-fbf7fb4f1787/items/4970/image?etag=1780565225463&width=500&height=500",
      "src": "https://h030.towardsdatascience.com/rest/v2/contentStores/d7d3a4ab-7caf-4383-bbd2-fbf7fb4f1787/items/4970/image?etag=1780565225463&width=500&height=500",
      "alt": "",
      "downloaded": false,
      "discovery": "saved_markdown",
      "retried_at": "2026-10-20T17:49:33Z"
    }
  ],
  "audio": [],
  "videos": [
    {
      "url": "https://www.youtube.com/c/TowardsDataScience",
      "kind": "channel_reference",
      "status": "reference_only"
    }
  ],
  "links": [
    {
      "type": "internal",
      "href": "https://towardsdatascience.com/"
    },
    {
      "type": "internal",
      "href": "https://towardsdatascience.com/latest/"
    },
    {
      "type": "internal",
      "href": "https://towardsdatascience.com/tag/editors-pick/"
    },
    {
      "type": "internal",
      "href": "https://towardsdatascience.com/tag/the-variable/"
    },
    {
      "type": "internal",
      "href": "https://towardsdatascience.com/shipai/"
    },
    {
      "type": "internal",
      "href": "https://towardsdatascience.com/submissions/"
    },
    {
      "type": "internal",
      "href": "https://towardsdatascience.com/category/data-science/"
    },
    {
      "type": "internal",
      "href": "https://towardsdatascience.com/author/chuachinhon/"
    },
    {
      "type": "internal",
      "href": "https://towardsdatascience.com/tag/data-science/"
    },
    {
      "type": "internal",
      "href": "https://towardsdatascience.com/tag/hugging-face/"
    },
    {
      "type": "internal",
      "href": "https://towardsdatascience.com/tag/nlp/"
    },
    {
      "type": "internal",
      "href": "https://towardsdatascience.com/tag/science/"
    },
    {
      "type": "internal",
      "href": "https://towardsdatascience.com/tag/singapore/"
    },
    {
      "type": "internal",
      "href": "https://towardsdatascience.com/exploratory-data-analysis-with-python-part-2-9b0b6c1402c2/"
    },
    {
      "type": "internal",
      "href": "https://towardsdatascience.com/author/gustavorsantos/"
    },
    {
      "type": "internal",
      "href": "https://towardsdatascience.com/downscaling-a-satellite-thermal-image-from-1000-m-to-10-m-python-3b2ed19ff103/"
    },
    {
      "type": "internal",
      "href": "https://towardsdatascience.com/author/mahyar.aboutalebi/"
    },
    {
      "type": "internal",
      "href": "https://towardsdatascience.com/sql-servers-secret-feature-run-python-and-add-ons-natively-in-sql-server-7f3c4efe5c00/"
    },
    {
      "type": "internal",
      "href": "https://towardsdatascience.com/category/artificial-intelligence/machine-learning/"
    },
    {
      "type": "internal",
      "href": "https://towardsdatascience.com/author/sasha.korovkina2003/"
    },
    {
      "type": "external",
      "href": "https://write.insightmediagroup.io"
    },
    {
      "type": "external",
      "href": "https://www.linkedin.com/company/towards-data-science/"
    },
    {
      "type": "external",
      "href": "https://x.com/TDataScience"
    },
    {
      "type": "external",
      "href": "https://www.pap.org.sg/"
    },
    {
      "type": "external",
      "href": "https://www.wp.sg/message-for-voters/"
    },
    {
      "type": "external",
      "href": "https://huggingface.co/transformers/_modules/transformers/pipelines.html"
    },
    {
      "type": "external",
      "href": "https://roadmap.sh/ai-data-scientist"
    },
    {
      "type": "external",
      "href": "https://roadmap.sh/machine-learning"
    },
    {
      "type": "external",
      "href": "https://roadmap.sh/computer-science"
    },
    {
      "type": "external",
      "href": "https://github.com/chuachinhon/practical_nlp"
    },
    {
      "type": "external",
      "href": "https://github.com/chuachinhon/practical_nlp/blob/master/data/ge2020_speeches.csv"
    },
    {
      "type": "external",
      "href": "https://github.com/chuachinhon/practical_nlp/blob/master/notebooks/2.0_speech_summary_cch.ipynb"
    },
    {
      "type": "external",
      "href": "https://huggingface.co/facebook/bart-large-cnn"
    },
    {
      "type": "external",
      "href": "https://huggingface.co/models?filter=pipeline%3Asummarization"
    },
    {
      "type": "external",
      "href": "https://pypi.org/project/transformers/"
    },
    {
      "type": "external",
      "href": "https://www.dropbox.com/s/bgdi2p7vs2d8igy/short_results.csv"
    },
    {
      "type": "external",
      "href": "https://www.pap.org.sg/news/ge2020-news/ge2020-lunch-time-rally/"
    },
    {
      "type": "external",
      "href": "http://a"
    },
    {
      "type": "external",
      "href": "https://www.dropbox.com/s/uqs14olj69mzntq/long_speech_results.csv"
    },
    {
      "type": "external",
      "href": "https://en.wikipedia.org/wiki/Lee_Kuan_Yew"
    }
  ],
  "repair_version": 2,
  "repaired_at": "2026-10-20T17:27:03Z",
  "source_url": "https://towardsdatascience.com/practical-nlp-summarising-short-and-long-speeches-with-hugging-faces-pipeline-bc7df76bd366/",
  "delivery_status": "unverified",
  "discovery_status": "http_403",
  "missing_reason": "源页面未重新核验:http_403；已按保留正文补采图片",
  "repair_error": "",
  "actual_video_files": [],
  "expected_saved_body_images": 4,
  "image_discovery": "saved_markdown",
  "source_body_markdown": "![Revisit consent button](https://cdn-cookieyes.com/assets/images/revisit.svg)\nWe value your privacy\nWe use cookies to enhance your browsing experience, serve personalised ads or content, and analyse our traffic. By clicking \"Accept All\", you consent to our use of cookies.\nCustomise Reject All Accept All\nCustomise Consent Preferences ![](https://cdn-cookieyes.com/assets/images/close.svg)\nWe use cookies to help you navigate efficiently and perform certain functions. You will find detailed information about all cookies under each consent category below.\nThe cookies that are categorised as \"Necessary\" are stored on your browser as they are essential for enabling the basic functionalities of the site. ... Show more\nNecessaryAlways Active\nNecessary cookies are required to enable the basic features of this site, such as providing secure log-in or adjusting your consent preferences. These cookies do not store any personally identifiable data.\n  * Cookie\nBCTempID\n  * Duration\n10 minutes\n  * Description\nNo description available.\n\n\n  * Cookie\n__cf_bm\n  * Duration\n1 hour\n  * Description\nThis cookie, set by Cloudflare, is used to support Cloudflare Bot Management. \n\n\n  * Cookie\nAWSALBCORS\n  * Duration\n7 days\n  * Description\nAmazon Web Services set this cookie for load balancing.\n\n\n  * Cookie\n_cfuvid\n  * Duration\nsession\n  * Description\nCloudflare sets this cookie to track users across sessions to optimize user experience by maintaining session consistency and providing personalized services\n\n\n  * Cookie\nli_gc\n  * Duration\n6 months\n  * Description\nLinkedin set this cookie for storing visitor's consent regarding using cookies for non-essential purposes.\n\n\n  * Cookie\n__hssrc\n  * Duration\nsession\n  * Description\nThis cookie is set by Hubspot whenever it changes the session cookie. The __hssrc cookie set to 1 indicates that the user has restarted the browser, and if the cookie does not exist, it is assumed to be a new session.\n\n\n  * Cookie\n__hssc\n  * Duration\n1 hour\n  * Description\nHubSpot sets this cookie to keep track of sessions and to determine if HubSpot should increment the session number and timestamps in the __hstc cookie.\n\n\n  * Cookie\nwpEmojiSettingsSupports\n  * Duration\nsession\n  * Description\nWordPress sets this cookie when a user interacts with emojis on a WordPress site. It helps determine if the user's browser can display emojis properly.\n\n\n  * Cookie\nBCSessionID\n  * Duration\n1 year 1 month 4 days\n  * Description\nBlueconic sets this cookie as a unique identifier for the BlueConic profile.\n\n\n  * Cookie\n_octo\n  * Duration\n1 year\n  * Description\nNo description available.\n\n\n  * Cookie\nlogged_in\n  * Duration\n1 year\n  * Description\nNo description available.\n\n\n  * Cookie\n__Secure-YEC\n  * Duration\npast\n  * Description\nYouTube sets this cookie to stores the user's video player preferences using embedded YouTube video\n\n\n  * Cookie\n__eoi\n  * Duration\n6 months\n  * Description\nDescription is currently not available.\n\n\n  * Cookie\nAWSALBTGCORS\n  * Duration\n7 days\n  * Description\nNo description available.\n\n\n  * Cookie\nlogin-status-p\n  * Duration\npast\n  * Description\nDescription is currently not available.\n\n\n  * Cookie\nAWSALBTG\n  * Duration\n7 days\n  * Description\nNo description available.\n\n\n  * Cookie\ncsrf_token\n  * Duration\nsession\n  * Description\nNo description available.\n\n\n  * Cookie\ntoken_v2\n  * Duration\n1 day\n  * Description\nDescription is currently not available.\n\n\n  * Cookie\nD\n  * Duration\n1 year\n  * Description\nDescription is currently not available.\n\n\n  * Cookie\nPHPSESSID\n  * Duration\nsession\n  * Description\nThis cookie is native to PHP applications. The cookie stores and identifies a user's unique session ID to manage user sessions on the website. The cookie is a session cookie and will be deleted when all the browser windows are closed.\n\n\n  * Cookie\nVISITOR_PRIVACY_METADATA\n  * Duration\n6 months\n  * Description\nYouTube sets this cookie to store the user's cookie consent state for the current domain. \n\n\n  * Cookie\ncookietest\n  * Duration\nsession\n  * Description\nThe cookietest cookie is typically used to determine whether the user's browser accepts cookies, essential for website functionality and user experience.\n\n\n  * Cookie\n__Host-airtable-session\n  * Duration\n1 year\n  * Description\nThis cookie is used to enable us to integrate the services of Airtable.\n\n\n  * Cookie\n__Host-airtable-session.sig\n  * Duration\n1 year\n  * Description\nThis cookie is used to enable us to integrate the services of Airtable.\n\n\n  * Cookie\nm\n  * Duration\n1 year 1 month 4 days\n  * Description\nStripe sets this cookie for fraud prevention purposes. It identifies the device used to access the website, allowing the website to be formatted accordingly.\n\n\n  * Cookie\nBIGipServer*\n  * Duration\nsession\n  * Description\nMarketo sets this cookie to collect information about the user's online activity and build a profile about their interests to provide advertisements relevant to the user. \n\n\n  * Cookie\n__cfruid\n  * Duration\nsession\n  * Description\nCloudflare sets this cookie to identify trusted web traffic.\n\n\n  * Cookie\n_GRECAPTCHA\n  * Duration\n6 months\n  * Description\nGoogle Recaptcha service sets this cookie to identify bots to protect the website against malicious spam attacks.\n\n\n  * Cookie\n__Secure-YNID\n  * Duration\n6 months\n  * Description\nGoogle cookie used to protect user security and prevent fraud, especially during the login process.\n\n\n  * Cookie\ncookieyes-consent\n  * Duration\n1 year\n  * Description\nCookieYes sets this cookie to remember users' consent preferences so that their preferences are respected on subsequent visits to this site. It does not collect or store any personal information about the site visitors.\n\n\nFunctional\nFunctional cookies help perform certain functionalities like sharing the content of the website on social media platforms, collecting feedback, and other third-party features.\n  * Cookie\nlidc\n  * Duration\n1 day\n  * Description\nLinkedIn sets the lidc cookie to facilitate data center selection.\n\n\n  * Cookie\nbrw\n  * Duration\n1 year\n  * Description\nNo description available.\n\n\n  * Cookie\nbrwConsent\n  * Duration\n5 minutes\n  * Description\nDescription is currently not available.\n\n\n  * Cookie\nWMF-Uniq\n  * Duration\n1 year\n  * Description\nDescription is currently not available.\n\n\n  * Cookie\nloom_anon_comment\n  * Duration\n1 year\n  * Description\nNo description available.\n\n\n  * Cookie\nloom_referral_video\n  * Duration\nsession\n  * Description\nDescription is currently not available.\n\n\n  * Cookie\nVISITOR_INFO1_LIVE\n  * Duration\n6 months\n  * Description\nA cookie set by YouTube to measure bandwidth that determines whether the user gets the new or old player interface.\n\n\n  * Cookie\nyt-remote-connected-devices\n  * Duration\nNever Expires\n  * Description\nYouTube sets this cookie to store the user's video preferences using embedded YouTube videos.\n\n\n  * Cookie\nytidb::LAST_RESULT_ENTRY_KEY\n  * Duration\nNever Expires\n  * Description\nThe cookie ytidb::LAST_RESULT_ENTRY_KEY is used by YouTube to store the last search result entry that was clicked by the user. This information is used to improve the user experience by providing more relevant search results in the future.\n\n\n  * Cookie\nyt-remote-device-id\n  * Duration\nNever Expires\n  * Description\nYouTube sets this cookie to store the user's video preferences using embedded YouTube videos.\n\n\n  * Cookie\nyt-remote-session-name\n  * Duration\nsession\n  * Description\nThe yt-remote-session-name cookie is used by YouTube to store the user's video player preferences using embedded YouTube video.\n\n\n  * Cookie\nyt-remote-fast-check-period\n  * Duration\nsession\n  * Description\nThe yt-remote-fast-check-period cookie is used by YouTube to store the user's video player preferences for embedded YouTube videos.\n\n\n  * Cookie\nyt-remote-session-app\n  * Duration\nsession\n  * Description\nThe yt-remote-session-app cookie is used by YouTube to store user preferences and information about the interface of the embedded YouTube video player.\n\n\n  * Cookie\nyt-remote-cast-available\n  * Duration\nsession\n  * Description\nThe yt-remote-cast-available cookie is used to store the user's preferences regarding whether casting is available on their YouTube video player.\n\n\n  * Cookie\nyt-remote-cast-installed\n  * Duration\nsession\n  * Description\nThe yt-remote-cast-installed cookie is used to store the user's video player preferences using embedded YouTube video.\n\n\n  * Cookie\ncp_session\n  * Duration\n3 months\n  * Description\nCodepen sets this cookie for Help systems found in the website.\n\n\n  * Cookie\nloid\n  * Duration\n1 year 1 month 4 days\n  * Description\nThis cookie is set by the Reddit. The cookie enables the sharing of content from the website onto the social media platform.\n\n\nAnalytics\nAnalytical cookies are used to understand how visitors interact with the website. These cookies help provide information on metrics such as the number of visitors, bounce rate, traffic source, etc.\n  * Cookie\n__hstc\n  * Duration\n6 months\n  * Description\nHubspot set this main cookie for tracking visitors. It contains the domain, initial timestamp (first visit), last timestamp (last visit), current timestamp (this visit), and session number (increments for each subsequent session).\n\n\n  * Cookie\nhubspotutk\n  * Duration\n6 months\n  * Description\nHubSpot sets this cookie to keep track of the visitors to the website. This cookie is passed to HubSpot on form submission and used when deduplicating contacts.\n\n\n  * Cookie\n_ga\n  * Duration\n1 year 1 month 4 days\n  * Description\nGoogle Analytics sets this cookie to calculate visitor, session and campaign data and track site usage for the site's analytics report. The cookie stores information anonymously and assigns a randomly generated number to recognise unique visitors.\n\n\n  * Cookie\n_ga_*\n  * Duration\n1 year 1 month 4 days\n  * Description\nGoogle Analytics sets this cookie to store and count page views.\n\n\n  * Cookie\n__Host-psifi.analyticsTrace\n  * Duration\n6 hours\n  * Description\nDescription is currently not available.\n\n\n  * Cookie\n__Host-psifi.analyticsTraceV2\n  * Duration\n6 hours\n  * Description\nDescription is currently not available.\n\n\n  * Cookie\n_gh_sess\n  * Duration\nsession\n  * Description\nGitHub sets this cookie for temporary application and framework state between pages like what step the user is on in a multiple step form.\n\n\n  * Cookie\nYSC\n  * Duration\nsession\n  * Description\nYSC cookie is set by Youtube and is used to track the views of embedded videos on Youtube pages.\n\n\n  * Cookie\najs_anonymous_id\n  * Duration\n1 year\n  * Description\nThis cookie is set by Segment to count the number of people who visit a certain site by tracking if they have visited before.\n\n\n  * Cookie\nvuid\n  * Duration\n1 year 1 month 4 days\n  * Description\nVimeo installs this cookie to collect tracking information by setting a unique ID to embed videos on the website. \n\n\nPerformance\nPerformance cookies are used to understand and analyse the key performance indexes of the website which helps in delivering a better user experience for the visitors.\n  * Cookie\nAWSALB\n  * Duration\n7 days\n  * Description\nAWSALB is an application load balancer cookie set by Amazon Web Services to map the session to the target.\n\n\n  * Cookie\nacq\n  * Duration\npast\n  * Description\nDescription is currently not available.\n\n\n  * Cookie\nacq.sig\n  * Duration\npast\n  * Description\nDescription is currently not available.\n\n\n  * Cookie\nptc\n  * Duration\n2 years\n  * Description\nNo description available.\n\n\nAdvertisement\nAdvertisement cookies are used to provide visitors with customised advertisements based on the pages you visited previously and to analyse the effectiveness of the ad campaigns.\n  * Cookie\nmuc_ads\n  * Duration\n1 year 1 month 4 days\n  * Description\nTwitter sets this cookie to collect user behaviour and interaction data to optimize the website.\n\n\n  * Cookie\nguest_id_marketing\n  * Duration\n1 year 1 month 4 days\n  * Description\nTwitter sets this cookie to identify and track the website visitor.\n\n\n  * Cookie\nguest_id_ads\n  * Duration\n1 year 1 month 4 days\n  * Description\nTwitter sets this cookie to identify and track the website visitor.\n\n\n  * Cookie\npersonalization_id\n  * Duration\n1 year 1 month 4 days\n  * Description\nTwitter sets this cookie to integrate and share features for social media and also store information about how the user uses the website, for tracking and targeting.\n\n\n  * Cookie\nguest_id\n  * Duration\n1 year 1 month 4 days\n  * Description\nTwitter sets this cookie to identify and track the website visitor. It registers if a user is signed in to the Twitter platform and collects information about ad preferences.\n\n\n  * Cookie\nbcookie\n  * Duration\n1 year\n  * Description\nLinkedIn sets this cookie from LinkedIn share buttons and ad tags to recognize browser IDs.\n\n\n  * Cookie\n__Secure-ROLLOUT_TOKEN\n  * Duration\n6 months\n  * Description\nYouTube sets this cookie to manage feature rollout and experimentation. It helps Google control which new features or interface changes are shown to users as part of testing and staged rollouts, ensuring consistent experience for a given user during an experiment.\n\n\n  * Cookie\nyt.innertube::nextId\n  * Duration\nNever Expires\n  * Description\nYouTube sets this cookie to register a unique ID to store data on what videos from YouTube the user has seen.\n\n\n  * Cookie\nyt.innertube::requests\n  * Duration\nNever Expires\n  * Description\nYouTube sets this cookie to register a unique ID to store data on what videos from YouTube the user has seen.\n\n\n  * Cookie\nsession_tracker\n  * Duration\nsession\n  * Description\nThis cookie is set by the Reddit. This cookie is used to identify trusted web traffic. It also helps in adverstising on the website.\n\n\n  * Cookie\nedgebucket\n  * Duration\nsession\n  * Description\nReddit sets this cookie to save the information about a log-on Reddit user, for the purpose of advertisement recommendations and updating the content.\n\n\n  * Cookie\ndid\n  * Duration\n1 year\n  * Description\nArbor sets this cookie to show targeted ads to site visitors.This cookie expires after 2 months or 1 year.\n\n\nUncategorised\nOther uncategorised cookies are those that are being analysed and have not been classified into a category as yet.\nNo cookies to display.\nReject All Save My Preferences Accept All\nToggle Search[](https://towardsdatascience.com/)Toggle Mobile Navigation\n[](https://towardsdatascience.com/)\nPublish AI, ML & data-science insights to a global community of data professionals.\nSign in[Submit an Article](https://write.insightmediagroup.io)\n[Latest](https://towardsdatascience.com/latest/)[Editor's Picks](https://towardsdatascience.com/tag/editors-pick/)[Newsletter](https://towardsdatascience.com/tag/the-variable/)[ShipAI](https://towardsdatascience.com/shipai/)\n* * *\n[Write For TDS](https://towardsdatascience.com/submissions/)\n[LinkedIn](https://www.linkedin.com/company/towards-data-science/)[X](https://x.com/TDataScience)Toggle Search\n[Data Science](https://towardsdatascience.com/category/data-science/)\n# Practical NLP: Summarising Short and Long Speeches With Hugging Face's Pipeline\nCompared to sentiment analysis or classification, text summarisation is a far less ubiquitous NLP task due to the time and resources...\n[Chua Chin Hon](https://towardsdatascience.com/author/chuachinhon/)\nJuly 27, 20206 min read\n### _Compared to sentiment analysis or classification, text summarisation is a far less ubiquitous NLP task due to the time and resources needed to execute it well. Hugging Face's transformers pipeline has changed that. Here's a quick demo of how you can summarise short and long speeches easily._\n![Screen grabs from PAP.org.sg \\(left\\) and WP.sg \\(right\\).](https://assets.insightmediagroup.io/media/wp-content/uploads/2020/07/1KMA8dQRCoIh_nEJsgYUkQw.jpeg)\nScreen grabs from [PAP.org.sg](https://www.pap.org.sg/) (left) and [WP.sg](https://www.wp.sg/message-for-voters/) (right).\nSummarising a speech is more art than science, some might argue. But recent advances in NLP could well test the validity of that argument.\nIn particular, Hugging Face's (HF) transformers [summarisation pipeline](https://huggingface.co/transformers/_modules/transformers/pipelines.html#SummarizationPipeline) has made the task easier, faster and more efficient to execute. Admittedly, there's still a hit-and-miss quality to current results. But there are also flashes of brilliance that hint at the possibilities to come as language models become more sophisticated.\nThis post will demonstrate how you can easily use HF's pipeline to summarise both short and long speeches. A minor work-around is needed for long speeches due to the maximum sequence limit for models used in the pipeline. I've also included the code for a simple text summarisation web app that you can run on your local machine.\n* * *\nLearn this step by step with the interactive [AI and Data Scientist](https://roadmap.sh/ai-data-scientist), [Machine Learning](https://roadmap.sh/machine-learning) and [Computer Science](https://roadmap.sh/computer-science) roadmaps.\n## 1. DATA, NOTEBOOK AND CAVEATS\nHere's the [Github repo](https://github.com/chuachinhon/practical_nlp) for this post. The [toy dataset used in this post](https://github.com/chuachinhon/practical_nlp/blob/master/data/ge2020_speeches.csv) is based on a small collection of political speeches/statements made during Singapore's General Election in July 2020. The word-count for the speeches, by politicians from the ruling People's Action Party (PAP) as well as the Opposition, range from 236 words to 3,746 words.\n![](https://assets.insightmediagroup.io/media/wp-content/uploads/2020/07/1xcxwZcGdLwfiF-0tydxeAQ.jpeg)\nThe simple workflow outlined in my [notebook ](https://github.com/chuachinhon/practical_nlp/blob/master/notebooks/2.0_speech_summary_cch.ipynb)should work for any other collection of speeches you care to put together in a CSV file. The HF summarisation pipeline doesn't work for non-English speeches as far as I know.\nI'm using the pipeline out of the box, meaning the results stem from the default [bart-large-cnn](https://huggingface.co/facebook/bart-large-cnn) model. Feel free to test with other [models tuned for this task](https://huggingface.co/models?filter=pipeline:summarization).\n## 2. SUMMARISING SHORT SPEECHES\nIt is unlikely that anyone needs to batch run summaries of dozens or hundreds of short speeches in one go. But in the off-chance that you do, the HF pipeline's ease of use is a lifesaver.\nHere, I ran the pipeline on seven speeches under 800 words. Results and time taken may vary according to the computer and version of the transformers library you are using.\nThis was run on a 2018 Mac Mini with 64Gb of RAM on version 2.11.0 of the [transformers library](https://pypi.org/project/transformers/). You can download the results [here](https://www.dropbox.com/s/bgdi2p7vs2d8igy/short_results.csv) and compare the summaries with the original text:\n![Download this CSV file here.](https://assets.insightmediagroup.io/media/wp-content/uploads/2020/07/111Sptw4xcGIx42ufaA2jLQ-scaled.jpeg)\nDownload this CSV file [here](https://www.dropbox.com/s/bgdi2p7vs2d8igy/short_results.csv).\nSome of the summaries are so-so. There's a clear tendency for the model to use the first and second sentences in a speech. This could work well for summaries of news stories, but less so for those involving political/corporate speeches.\nBut the model turned up one brilliant summary, the fifth one w[here](https://www.wp.sg/message-for-voters/) Workers' Party (WP) leader Pritam Singh laid out his party's main pitch to voters. See the original statement here, and here's a side-by-side comparison with the summary by the Bart model.\n![Image on left: Screen grab from WP.sg.](https://assets.insightmediagroup.io/media/wp-content/uploads/2020/07/1TTVJJA7anjO5nzQ5euf1SQ.jpeg)\nImage on left: Screen grab from [WP.sg](https://www.wp.sg/message-for-voters/).\nIn just 59 words, the Bart model perfectly distilled the essence of the WP's campaign message from a 627-word statement. Those who are familiar with Singapore politics would appreciate how sharp this summary is, particularly given that the model was not tuned on data involving Singapore news or politics.\nIt is not unreasonable to ask whether this summary is a fluke or happy coincidence. I ran the same set of speeches through the latest version of transformers (version 3.0.2) and got a less impressive translation: \"The workers party offers singaporeans a choice and an alternative voice in parliament . The wp has contributed significantly to the democratic processes in our country . Former pap mps who wrote their memoirs after have helped to expose the limitations of a parliament dominated by the pap\".\nConsistency of quality appears to be a problem at this point. A fuller test, unfortunately, is outside the scope of this post.\n## 3. SUMMARISING A LONG SPEECH\nThis is likely the use case for most people: Summarising a long speech running into thousands of words. To get around the sequence length limits in the pipeline/models, I used a function to break up the text into a number of chunks of fixed-character length.\nTo keep this post concise, I decided to run the HF pipeline on the longest speech - 3,746 words - in the toy dataset. I broke up the speech into 9 chunks of 2,500 characters each.\nThe function allows you to easily set the character length that you prefer. This approach is admittedly clumsy, but I find it useful for a closer inspection of the results. Details are in my [notebook](https://github.com/chuachinhon/practical_nlp/blob/master/notebooks/2.0_speech_summary_cch.ipynb).\nHere's the [link to the original speech](https://www.pap.org.sg/news/ge2020-news/ge2020-lunch-time-rally/), and a CSV file comparing the original text with the mini-summaries can be dowloaded [here](<http://A> CSV file comparing the original text with the mini-summaries can be dowloaded here). This is a side-by-side comparison of how the model summarised it :\n![Image on left: Screen grab from Pap.org.sg. Download the CSV results file of the long-speech summarisation here.](https://assets.insightmediagroup.io/media/wp-content/uploads/2020/07/1gzDMp3_7XeNjAoECxB8wKA.jpeg)\nImage on left: Screen grab from [Pap.org.sg](https://www.pap.org.sg/). Download the CSV results file of the long-speech summarisation [here](https://www.dropbox.com/s/uqs14olj69mzntq/long_speech_results.csv).\nThe 536-word \"combined summary\" is not as brilliant as the WP example I highlighted above, but it's pretty decent (except for the section highlighted in red, which I'll discuss in a bit) for a first draft. If I'm in a crunch, this is something I can quickly edit into a more useable form.\nSingaporean users will easily pick out an obvious mistake in the summary of the sixth section: \"Pap Lee Kuan Yew seeks not just your mandate but your strong mandate to lead singapore through this crisis.\"\nThe original paragraph reads: \"Investors will scrutinise the election results, and act on their conclusions. So will others, both friends and adversaries of Singapore. That is why in this election, the PAP seeks not just your mandate, but your strong mandate, to lead Singapore through this crisis.\"\nLee Kuan Yew, Singapore's founding prime minister, [died in March 2015](https://en.wikipedia.org/wiki/Lee_Kuan_Yew). However, those with more than a passing knowledge of Singapore politics might flippantly argue that the model was in fact correct and not erroneous with the above summary mentioning Lee Kuan Yew. But that is a discussion for another day ;p.\n## 4. WEB APP\nI tried to deploy a simple web app to let non-notebook users try the summarisation pipeline, but discovered that [free accounts on Pythonanywhere.com don't have enough storage space](https://www.pythonanywhere.com/forums/topic/26685/) for the libraries required, particularly pytorch.\nSo instead I uploaded a [version that runs on local machines](https://github.com/chuachinhon/practical_nlp/blob/master/apps/summary_app_local.py), if you are keen to let colleagues at the workplace try it out. The code for the web app was adapted from two sources: the [main one](https://github.com/Csmith715/Summarizer) from Github and a [second one by Plotly](https://github.com/plotly/dash-sample-apps/blob/master/apps/dash-summarize/app.py) using DistilBart.\n![](https://assets.insightmediagroup.io/media/wp-content/uploads/2020/07/1LyjlB15Y9BFTEvUbwS_gog.gif)\nIf the app returns an error, it means the text you've copied has exceeded the maximum number of tokens. Try summarising in smaller chunks or use the function in the notebook to break up the text. I'll try to deploy the app online in future if Pythonanywhere.com includes pytorch as part of their pre-installed packages.\n## 5. CONCLUSION\nWith more language models being added by the week, there's ample scope for experimentation and more robust tests of the HF pipeline's summarisation output. My own experience so far is that using a different model doesn't necessarily translate into better results.\nTowards the end of my notebook, I switched to the T[5 mode](https://huggingface.co/transformers/model_doc/t5.html)l for a trial but found that the summarisations for three short speeches weren't better than those churned out by the default Bart model.\nBut we are certainly getting a good glimpse of the future possibilities as companies like Hugging Face and OpenAI continue to push the NLP envelope. I'm certainly curious to see how GPT-3 performs on this task, given the [impressive results](https://gpt3examples.com/) it has achieved on a diverse range of tasks so far.\nMeanwhile, those who want to experiment further by fine tuning a transformer model with their own dataset can check out these two notebooks [[here](https://github.com/ohmeow/ohmeow_website/blob/master/_notebooks/2020-05-23-text-generation-with-blurr.ipynb)](https://github.com/abhimishra91/transformers-tutorials/blob/master/transformers_summarization_wandb.ipynb) and here. Finding a good dataset for your use case, however, could be more challenging than you think.\nIf you spot errors in this post or the notebook, ping me at:\nTwitter: [Chua Chin Hon](https://medium.com/u/b3d8090c0aee?source=post_page-----aad7f2e1d0a0----------------------)\nLinkedIn: [www.linkedin.com/in/chuachinhon](http://www.linkedin.com/in/chuachinhon)\n## Register Now to Continue Reading\n### Unlock Towards Data Science with a Free Account\nAll articles on Towards Data Science are available to registered users. Create a free account today to access this article and thousands more.\nRegister for freeSign in to your account\n* * *\n![](https://assets.insightmediagroup.io/media/1787657280763_838i2j.png)\nWritten By\n## [Chua Chin Hon](https://towardsdatascience.com/author/chuachinhon/)\n[See all from Chua Chin Hon](https://towardsdatascience.com/author/chuachinhon/)\n[Data Science](https://towardsdatascience.com/tag/data-science/)[Hugging Face](https://towardsdatascience.com/tag/hugging-face/)[NLP](https://towardsdatascience.com/tag/nlp/)[Science](https://towardsdatascience.com/tag/science/)[Singapore](https://towardsdatascience.com/tag/singapore/)\nShare This Article\n  * [](https://www.facebook.com/sharer/sharer.php?u=https%3A%2F%2Ftowardsdatascience.com%2Fpractical-nlp-summarising-short-and-long-speeches-with-hugging-faces-pipeline-bc7df76bd366%2F&title=Practical%20NLP%3A%20Summarising%20Short%20and%20Long%20Speeches%20With%20Hugging%20Face's%20Pipeline)\n  * [](https://www.linkedin.com/shareArticle?mini=true&url=https%3A%2F%2Ftowardsdatascience.com%2Fpractical-nlp-summarising-short-and-long-speeches-with-hugging-faces-pipeline-bc7df76bd366%2F&title=Practical%20NLP%3A%20Summarising%20Short%20and%20Long%20Speeches%20With%20Hugging%20Face's%20Pipeline)\n  * [](https://x.com/share?url=https%3A%2F%2Ftowardsdatascience.com%2Fpractical-nlp-summarising-short-and-long-speeches-with-hugging-faces-pipeline-bc7df76bd366%2F&text=Practical%20NLP%3A%20Summarising%20Short%20and%20Long%20Speeches%20With%20Hugging%20Face's%20Pipeline)\n\n\nTowards Data Science is a community publication. Submit your insights to reach our global audience and earn through the TDS Author Payment Program.\n[Write for TDS](https://towardsdatascience.com/submissions/)\n## Related Articles\n  * [![](https://assets.insightmediagroup.io/media/wp-content/uploads/2021/01/1QRMPny9g9mP9ADSvaYzung-scaled.jpeg)](https://towardsdatascience.com/exploratory-data-analysis-with-python-part-2-9b0b6c1402c2/)\n## [Exploratory Data Analysis with Python - Part 2](https://towardsdatascience.com/exploratory-data-analysis-with-python-part-2-9b0b6c1402c2/)\n[Data Science](https://towardsdatascience.com/category/data-science/)\nA template to follow to get you started analyzing data with Python and Pandas.\n[Gustavo Santos](https://towardsdatascience.com/author/gustavorsantos/)\nNovember 29, 20211 min read\n  * [![](https://assets.insightmediagroup.io/media/wp-content/uploads/2024/03/1jUCTi-gNlH_wB6sDYpoB6Q.png)](https://towardsdatascience.com/downscaling-a-satellite-thermal-image-from-1000-m-to-10-m-python-3b2ed19ff103/)\n## [Downscaling a Satellite Thermal Image from 1000 m to 10 m (Python)](https://towardsdatascience.com/downscaling-a-satellite-thermal-image-from-1000-m-to-10-m-python-3b2ed19ff103/)\n[Data Science](https://towardsdatascience.com/category/data-science/)\nThermal Sharpening of Sentinel-3 Images: From 1 Km to 10 m Using Python in Google Colab\n[Mahyar Aboutalebi](https://towardsdatascience.com/author/mahyar.aboutalebi/)\nMarch 6, 202413 min read\n  * [![](https://assets.insightmediagroup.io/media/wp-content/uploads/2024/05/0_zZKk7cPes8UaiJ_-scaled.jpg)](https://towardsdatascience.com/sql-servers-secret-feature-run-python-and-add-ons-natively-in-sql-server-7f3c4efe5c00/)\n## [SQL Server's Secret Feature - Run Python and Add-Ons Natively In SQL Server.](https://towardsdatascience.com/sql-servers-secret-feature-run-python-and-add-ons-natively-in-sql-server-7f3c4efe5c00/)\n[Machine Learning](https://towardsdatascience.com/category/artificial-intelligence/machine-learning/)\nImport Python libraries, manipulate and output SQL tables and more, all without leaving SQL server.\n[Sasha Korovkina](https://towardsdatascience.com/author/sasha.korovkina2003/)\nMay 15, 20244 min read\n  * [![](https://assets.insightmediagroup.io/media/wp-content/uploads/2022/01/1rtSbj_ROpjYEEf_Ql8QjrA-scaled.jpeg)](https://towardsdatascience.com/python-pandas-gives-you-full-control-over-timestamps-10fe26c96a9e/)\n## [Python Pandas Gives You Full Control Over Timestamps](https://towardsdatascience.com/python-pandas-gives-you-full-control-over-timestamps-10fe26c96a9e/)\n[Artificial Intelligence](https://towardsdatascience.com/category/artificial-intelligence/)\nFrom years to nanoseconds.\n[Soner Yıldırım](https://towardsdatascience.com/author/sonery/)\nJanuary 15, 20226 min read\n  * [![](https://assets.insightmediagroup.io/media/wp-content/uploads/2021/07/1_CQ9x0sI4UNTKh0q5wU6Xg.jpeg)](https://towardsdatascience.com/five-rules-for-designing-tables-a953a16e50f3/)\n## [Five Rules for Designing Tables](https://towardsdatascience.com/five-rules-for-designing-tables-a953a16e50f3/)\n[Data Science](https://towardsdatascience.com/category/data-science/)\nSimple design elements to make your spreadsheets pop\n[Alana Pirrone](https://towardsdatascience.com/author/alanapirrone/)\nJuly 27, 20214 min read\n  * [![](https://assets.insightmediagroup.io/media/wp-content/uploads/2021/06/1vFyFUBC4kjqZDM5WmlFVcQ.gif)](https://towardsdatascience.com/how-to-optimize-your-switchback-a-b-test-configuration-791a28bee678/)\n## [How to Optimize your Switchback A/B Test Configuration](https://towardsdatascience.com/how-to-optimize-your-switchback-a-b-test-configuration-791a28bee678/)\n[Data Science](https://towardsdatascience.com/category/data-science/)\nAn algorithm that determines the most effective randomization points for a switchback experiment.\n[Michael Berk](https://towardsdatascience.com/author/michaelberk/)\nJune 8, 20219 min read\n  * [![](https://assets.insightmediagroup.io/media/wp-content/uploads/2022/06/0Jx1WojSX2JLJ6BeE-scaled.jpg)](https://towardsdatascience.com/simple-explanations-of-basic-statistics-concepts-part-1-bba66a1761d7/)\n## [Simple Explanations of Basic Statistics Concepts (Part 1)](https://towardsdatascience.com/simple-explanations-of-basic-statistics-concepts-part-1-bba66a1761d7/)\n[Data Science](https://towardsdatascience.com/category/data-science/)\nSimple and comprehensive explanations of different statistical concepts\n[Chi Nguyen](https://towardsdatascience.com/author/nphchi223/)\nJune 7, 20227 min read\n  * [![](https://assets.insightmediagroup.io/media/wp-content/uploads/2022/11/0OLY3ywEDsupO4n2I.jpg)](https://towardsdatascience.com/a-guide-to-using-h2o-ai-in-r-99cf6265bc05/)\n## [A Guide to using h2o.ai in R](https://towardsdatascience.com/a-guide-to-using-h2o-ai-in-r-99cf6265bc05/)\n[Machine Learning](https://towardsdatascience.com/category/artificial-intelligence/machine-learning/)\nLearn how to work with the h2o library using the R Language\n[Ivo Bernardo](https://towardsdatascience.com/author/ivopbernardo/)\nNovember 2, 202215 min read\n  * [![](https://assets.insightmediagroup.io/media/wp-content/uploads/2021/04/0wE2-Dt7Vrb68FfXB-scaled.jpg)](https://towardsdatascience.com/why-you-shouldnt-hire-more-data-scientists-3188a1597fa3/)\n## [Why You Shouldn't Hire More Data Scientists](https://towardsdatascience.com/why-you-shouldnt-hire-more-data-scientists-3188a1597fa3/)\n[Data Science](https://towardsdatascience.com/category/data-science/)\nSave money and frustration by considering the roles of analysts, data engineers, and software engineers...\n[Adam Sroka](https://towardsdatascience.com/author/aesroka/)\nApril 26, 20217 min read\n\n\n  * [YouTube](https://www.youtube.com/c/TowardsDataScience)\n  * [X](https://x.com/TDataScience)\n  * [LinkedIn](https://www.linkedin.com/company/towards-data-science/)\n  * [Threads](https://www.threads.net/@towardsdatascience)\n  * [Bluesky](https://bsky.app/profile/towardsdatascience.com)\n\n\n[](https://towardsdatascience.com/)\nYour home for data science and AI. The world's leading publication for data science, data analytics, data engineering, machine learning, and artificial intelligence professionals.\nSubscribe to Our Newsletter[Write For TDS](https://towardsdatascience.com/submissions/)•[About](https://towardsdatascience.com/about-towards-data-science-d691af11cc2f/)•[Advertise](https://contact.towardsdatascience.com/advertise-with-towards-data-science)•[Privacy Policy](https://towardsdatascience.com/privacy-policy/)•[Terms of Use](https://towardsdatascience.com/website-terms-of-use/)\n© Insight Media Group, LLC 2026•[The New Stack](https://thenewstack.io)•[roadmap.sh](https://roadmap.sh)•[Creator Network](https://insightmediagroup.io/creator-network)\n####  Recommended Articles \nClose\n  * [ ![](https://h030.towardsdatascience.com/rest/v2/contentStores/d7d3a4ab-7caf-4383-bbd2-fbf7fb4f1787/items/607639/image?etag=1781250125848&width=500&height=500) Javascript Fatigue: HTMX Is All You Need to Build ChatGPT — Part 1 ](https://towardsdatascience.com/javascript-fatigue-you-dont-need-js-to-build-chatgpt/ \"Javascript Fatigue: HTMX Is All You Need to Build ChatGPT — Part 1\")\n  * [ ![](https://h030.towardsdatascience.com/rest/v2/contentStores/d7d3a4ab-7caf-4383-bbd2-fbf7fb4f1787/items/607930/image?etag=1782124566054&width=500&height=500) The Subset Sum Problem Solved in Linear Time for Dense Enough Inputs ](https://towardsdatascience.com/subset-sum-problem-solved-in-linear-time-for-dense-enough-inputs/ \"The Subset Sum Problem Solved in Linear Time for Dense Enough Inputs\")\n  * [ ![](https://h030.towardsdatascience.com/rest/v2/contentStores/d7d3a4ab-7caf-4383-bbd2-fbf7fb4f1787/items/3660/image?etag=1780176246070&width=500&height=500) Does Your Company Have a Data Strategy? ](https://towardsdatascience.com/does-your-company-have-a-data-strategy-e8944dbec4ae/ \"Does Your Company Have a Data Strategy?\")\n  * [ ![](https://h030.towardsdatascience.com/rest/v2/contentStores/d7d3a4ab-7caf-4383-bbd2-fbf7fb4f1787/items/4970/image?etag=1780565225463&width=500&height=500) Productivity Tips, Data Career Insights, and Other Recent Must-Reads ](https://towardsdatascience.com/productivity-tips-data-career-insights-and-other-recent-must-reads-5c47eb7a2fe0/ \"Productivity Tips, Data Career Insights, and Other Recent Must-Reads\")\n\n\n",
  "body_base_path": "attachments/d7f4b19b8b0f/pages/",
  "body_markdown_path": "attachments/d7f4b19b8b0f/pages/archived-body.md",
  "local_html": "attachments/d7f4b19b8b0f/pages/archived-body.html",
  "body_source": "prior_capture"
}