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{"emilylearning--cond_ft_none_on_reddit__prcnt_100__test_run_False__xlm-roberta-base": {
"description": "\nThis corpus contains preprocessed posts from the Reddit dataset.\nThe dataset consists of 3,848,330 posts with an average length of 270 words for content,\nand 28 words for the summary.\n\nFeatures includes strings: author, body, normalizedBody, content, summary, subreddit, subreddit_id.\nContent is used as document and summary is used as summary.\n",
"citation": "\n@inproceedings{volske-etal-2017-tl,\n title = {TL;DR: Mining {R}eddit to Learn Automatic Summarization},\n author = {V{\"o}lske, Michael and Potthast, Martin and Syed, Shahbaz and 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}\n",
"homepage": "https://github.com/webis-de/webis-tldr-17-corpus",
"license": "",
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},
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"dataset_name": "cond_ft_none_on_reddit__prcnt_100__test_run_False__xlm-roberta-base"
}
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}} |