Efficient Attentions for Long Document Summarization

• URL: http://arxiv.org/abs/2104.02112v1
• Date: Mon, 5 Apr 2021 18:45:13 GMT
• Title: Efficient Attentions for Long Document Summarization
• Authors: Luyang Huang, Shuyang Cao, Nikolaus Parulian, Heng Ji and Lu Wang
• Abstract summary: Hepos is a novel efficient encoder-decoder attention with head-wise positional strides. We are able to process ten times more tokens than existing models that use full attentions.
• Score: 25.234852272297598
• License: http://creativecommons.org/licenses/by/4.0/
• 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 with fewer unfaithful errors.

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