{
 "id": "webis/tldr-17",
 "owner": "webis",
 "name": "tldr-17",
 "downloads": 1386,
 "likes": 58,
 "last_modified": "2023-06-05T12:48:30.000Z",
 "stated_licence": "cc-by-4.0",
 "stated_from": "tag",
 "licence_name": null,
 "licence_link": null,
 "file_licence": null,
 "licence_files": [],
 "sources": [],
 "gated": false,
 "tasks": [
  "summarization"
 ],
 "citation": "@inproceedings{volske-etal-2017-tl,\n    title = \"{TL};{DR}: Mining {R}eddit to Learn Automatic Summarization\",\n    author = {V{\"o}lske, Michael  and\n      Potthast, Martin  and\n      Syed, Shahbaz  and\n      Stein, Benno},\n    booktitle = \"Proceedings of the Workshop on New Frontiers in Summarization\",\n    month = sep,\n    year = \"2017\",\n    address = \"Copenhagen, Denmark\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://www.aclweb.org/anthology/W17-4508\",\n    doi = \"10.18653/v1/W17-4508\",\n    pages = \"59--63\",\n    abstract = \"Recent advances in automatic text summarization have used deep neural networks to generate high-quality abstractive summaries, but the performance of these models strongly depends on large amounts of suitable training data. We propose a new method for mining social media for author-provided summaries, taking advantage of the common practice of appending a {``}TL;DR{''} to long posts. A case study using a large Reddit crawl yields the Webis-TLDR-17 dataset, complementing existing corpora primarily from the news genre. Our technique is likely applicable to other social media sites and general web crawls.\",\n}",
 "signal": "permissive",
 "reason": "The declared licence (cc-by-4.0) is classified as permissive.",
 "revision": "fa7f50e62d35aff41aa165ddbe6c10dfa01ff49c",
 "stated_evidence": "https://huggingface.co/datasets/webis/tldr-17/blob/fa7f50e62d35aff41aa165ddbe6c10dfa01ff49c/README.md",
 "file_evidence": "https://huggingface.co/datasets/webis/tldr-17/tree/fa7f50e62d35aff41aa165ddbe6c10dfa01ff49c",
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}