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  1. ; ; ; ; ; ( , IEEE robotics automation letters)
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  2. ; ; ; ; ; ( , IEEE Robotics and Automation Letters)
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  3. ; ; ; ; ( , Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics (NAACL))
    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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