Efficient Attentions for Long Document Summarization

Luyang Huang, Shuyang Cao, Nikolaus Parulian, Heng Ji, Lu Wang


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.
Anthology ID:
2021.naacl-main.112
Volume:
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Month:
June
Year:
2021
Address:
Online
Editors:
Kristina Toutanova, Anna Rumshisky, Luke Zettlemoyer, Dilek Hakkani-Tur, Iz Beltagy, Steven Bethard, Ryan Cotterell, Tanmoy Chakraborty, Yichao Zhou
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1419–1436
Language:
URL:
https://aclanthology.org/2021.naacl-main.112
DOI:
10.18653/v1/2021.naacl-main.112
Bibkey:
Cite (ACL):
Luyang Huang, Shuyang Cao, Nikolaus Parulian, Heng Ji, and Lu Wang. 2021. Efficient Attentions for Long Document Summarization. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 1419–1436, Online. Association for Computational Linguistics.
Cite (Informal):
Efficient Attentions for Long Document Summarization (Huang et al., NAACL 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.naacl-main.112.pdf
Video:
 https://aclanthology.org/2021.naacl-main.112.mp4
Code
 luyang-huang96/LongDocSum
Data
GovReport