{
 "id": "launch/gov_report",
 "owner": "launch",
 "name": "gov_report",
 "downloads": 3131,
 "likes": 15,
 "last_modified": "2022-11-09T01:58:24.000Z",
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 "licence_name": null,
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 "tasks": [
  "summarization"
 ],
 "citation": "@inproceedings{huang-etal-2021-efficient,\n    title = \"Efficient Attentions for Long Document Summarization\",\n    author = \"Huang, Luyang  and\n      Cao, Shuyang  and\n      Parulian, Nikolaus  and\n      Ji, Heng  and\n      Wang, Lu\",\n    booktitle = \"Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies\",\n    month = jun,\n    year = \"2021\",\n    address = \"Online\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://aclanthology.org/2021.naacl-main.112\",\n    doi = \"10.18653/v1/2021.naacl-main.112\",\n    pages = \"1419--1436\",\n    abstract = \"The quadratic computational and memory complexities of large Transformers have limited their scalability for long document summarization. In this paper, we propose Hepos, a novel efficient encoder-decoder attention with head-wise positional strides to effectively pinpoint salient information from the source. We further conduct a systematic study of existing efficient self-attentions. Combined with Hepos, we are able to process ten times more tokens than existing models that use full attentions. For evaluation, we present a new dataset, GovReport, with significantly longer documents and summaries. Results show that our models produce significantly higher ROUGE scores than competitive comparisons, including new state-of-the-art results on PubMed. Human evaluation also shows that our models generate more informative summaries wit",
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