| { | |
| "overview": { | |
| "where": { | |
| "has-leaderboard": "no", | |
| "leaderboard-url": "N/A", | |
| "leaderboard-description": "N/A", | |
| "website": "n/a", | |
| "data-url": "[Github](https://github.com/EdinburghNLP/XSum)", | |
| "paper-url": "[ACL Anthology](https://www.aclweb.org/anthology/D18-1206)", | |
| "paper-bibtext": "```\n@InProceedings{xsum-emnlp,\n author = \"Shashi Narayan and Shay B. Cohen and Mirella Lapata\",\n title = \"Don't Give Me the Details, Just the Summary! {T}opic-Aware Convolutional Neural Networks for Extreme Summarization\",\n booktitle = \"Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing \",\n year = \"2018\",\n address = \"Brussels, Belgium\",\n}\n```", | |
| "contact-name": "Shashi Narayan", | |
| "contact-email": "shashinarayan@google.com" | |
| }, | |
| "languages": { | |
| "is-multilingual": "no", | |
| "license": "cc-by-sa-4.0: Creative Commons Attribution Share Alike 4.0 International", | |
| "task-other": "N/A", | |
| "language-names": [ | |
| "English" | |
| ], | |
| "language-dialects": "Since the source of the dataset are BBC articles, the language is in British English of the variation written by journalists.", | |
| "language-speakers": "Professional journalists", | |
| "intended-use": "The dataset is for the task of abstractive summarization in its extreme form, its about summarizing a document in a single sentence. The idea is to create a short, one-sentence news summary answering the question \"What is the article about?\".\n\n", | |
| "license-other": "N/A", | |
| "task": "Summarization", | |
| "communicative": "Given a news article, produce a single sentence summary of the content of the article. " | |
| }, | |
| "credit": { | |
| "organization-type": [ | |
| "academic" | |
| ], | |
| "organization-names": "University of Edinburgh", | |
| "creators": "Shashi Narayan, Shay B. Cohen, Mirella Lapata (all affiliated with University of Edinburgh at the time of dataset creation)", | |
| "funding": "European Research Council (Lapata; award number 681760), the European Union under the Horizon 2020 SUMMA project (Narayan, Cohen; grant agreement 688139), and Huawei Technologies (Cohen).", | |
| "gem-added-by": "The original data card was written by Laura Perez-Beltrachini and the data loader by Yacine Jernite. Sebastian Gehrmann migrated the data card to the new format and extended it. The v2 data loader was migrated by Abinaya Mahendiran" | |
| }, | |
| "structure": { | |
| "data-fields": "- `Document`: Input news article.\n- `Summary`: One sentence summary of the article.\n- `Id`: BBC ID of the article.", | |
| "structure-description": "The Document/Summary format is standard for summarization datasets.", | |
| "structure-labels": "The labels are the first sentence of the source article. ", | |
| "structure-example": "```\n{\n 'document': 'The researchers have sequenced the genome of a strain of bacterium that causes the virulent infection.\\nA survey in 2007 showed that bleeding canker had spread rapidly, with almost half of the two million horse chestnuts displaying symptoms of the disease.\\nThe findings have been published in the journal PLoS One.\\nA visible symptom of the disease is a lesion on the bark, which oozes a resin on to the trunk or sometimes the branches.\\nThe bark underneath the canker is killed, and if cankers manage to go all the way around the trunk then the horse chestnut (Aesculus hippocastanum) will die because it cuts off the food supply. [...]',\n 'target': \"A team of UK scientists hopes to shed light on the mysteries of bleeding canker, a disease that is threatening the nation's horse chestnut trees.\",\n}\n```", | |
| "structure-splits": "| Section | Number of Documents |\n| ------------- |:-------------:|\n| Training | 204,045 |\n| Validation | 11,332 |\n| Testing | 11,334 |\n| Total | 226k |\n\n| Section | number of words| number of sentences |\n| ------------- |:-------------:| :-------------:|\n| Documents | 431.07 | 19.77 |\n| Summary | 23.26 | 1.00 |\n", | |
| "structure-splits-criteria": "The identifiers in the URLs were used to randomly split the dataset into training (90%, 204,045), validation (5%, 11,332), and test (5%, 11,334) sets.", | |
| "structure-outlier": "n/a" | |
| }, | |
| "what": { | |
| "dataset": "XSum is an English news summarization dataset where the task is to predict the first sentence of an article from the rest of it." | |
| } | |
| }, | |
| "curation": { | |
| "original": { | |
| "is-aggregated": "no", | |
| "aggregated-sources": "N/A", | |
| "rationale": "Comparable datasets are often very extractive which is not a strategy that works for one-sentence summaries. The dataset curators thus created this dataset as a way to evaluate truly abstractive models", | |
| "communicative": "Same as the communicative goal in GEM: A model should summarize a news article in a single sentence" | |
| }, | |
| "language": { | |
| "found": [ | |
| "Single website" | |
| ], | |
| "crowdsourced": [], | |
| "created": "N/A", | |
| "machine-generated": "N/A", | |
| "validated": "not validated", | |
| "is-filtered": "not filtered", | |
| "filtered-criteria": "N/A", | |
| "obtained": [ | |
| "Found" | |
| ], | |
| "producers-description": "The data was collected from articles between 2010 and 2017. No other information ", | |
| "topics": "The collected articles included the following topics: News, Politics, Sports, Weather, Business, Technology, Science, Health, Family, Education, Entertainment and Arts \n\nThe dataset curators also used LDA to gain insight into this question and found that the following were the top keywords associated with each topic:\n\n- **T1**: charge, court, murder, police, arrest, guilty, sentence, boy, bail, space, crown, trial\n- **T2**: church, abuse, bishop, child, catholic, gay, pope, school, christian, priest, cardinal\n- **T3**: council, people, government, local, housing, home, house, property, city, plan, authority\n- **T4**: clinton, party, trump, climate, poll, vote, plaid, election, debate, change, candidate, campaign\n- **T5**: country, growth, report, business, export, fall, bank, security, economy, rise, global, inflation\n- **T6**: hospital, patient, trust, nhs, people, care, health, service, staff, report, review, system, child", | |
| "pre-processed": "The text was extracted from the HTML of the webpage. No further processing was done." | |
| }, | |
| "annotations": { | |
| "origin": "none", | |
| "rater-number": "N/A", | |
| "rater-qualifications": "N/A", | |
| "rater-training-num": "N/A", | |
| "rater-test-num": "N/A", | |
| "rater-annotation-service-bool": "no", | |
| "rater-annotation-service": [], | |
| "values": "N/A", | |
| "quality-control": [], | |
| "quality-control-details": "N/A" | |
| }, | |
| "consent": { | |
| "has-consent": "no", | |
| "consent-policy": "N/A", | |
| "consent-other": "N/A", | |
| "no-consent-justification": "The copyright license of the data allows reusing it for this purpose. " | |
| }, | |
| "pii": { | |
| "has-pii": "yes/very likely", | |
| "no-pii-justification": "N/A", | |
| "is-pii-identified": "no identification", | |
| "pii-identified-method": "N/A", | |
| "is-pii-replaced": "N/A", | |
| "pii-replaced-method": "N/A", | |
| "pii-categories": [ | |
| "generic PII" | |
| ] | |
| }, | |
| "maintenance": { | |
| "has-maintenance": "no", | |
| "description": "N/A", | |
| "contact": "N/A", | |
| "contestation-mechanism": "N/A", | |
| "contestation-link": "N/A", | |
| "contestation-description": "N/A" | |
| } | |
| }, | |
| "context": { | |
| "previous": { | |
| "is-deployed": "no", | |
| "described-risks": "N/A", | |
| "changes-from-observation": "N/A" | |
| }, | |
| "underserved": { | |
| "helps-underserved": "no", | |
| "underserved-description": "N/A" | |
| }, | |
| "biases": { | |
| "has-biases": "unsure", | |
| "bias-analyses": "N/A", | |
| "speaker-distibution": "The language and content of the data is focused on news and language in the UK and as such not representative of the speakers world-wide. Existing selection biases of the BBC exist in this dataset." | |
| } | |
| } | |
| } |