Get To The Point: Summarization with Pointer-Generator Networks

Abigail See, Peter J. Liu, Christopher D. Manning


Abstract
Neural sequence-to-sequence models have provided a viable new approach for abstractive text summarization (meaning they are not restricted to simply selecting and rearranging passages from the original text). However, these models have two shortcomings: they are liable to reproduce factual details inaccurately, and they tend to repeat themselves. In this work we propose a novel architecture that augments the standard sequence-to-sequence attentional model in two orthogonal ways. First, we use a hybrid pointer-generator network that can copy words from the source text via pointing, which aids accurate reproduction of information, while retaining the ability to produce novel words through the generator. Second, we use coverage to keep track of what has been summarized, which discourages repetition. We apply our model to the CNN / Daily Mail summarization task, outperforming the current abstractive state-of-the-art by at least 2 ROUGE points.
Anthology ID:
P17-1099
Volume:
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Month:
July
Year:
2017
Address:
Vancouver, Canada
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1073–1083
Language:
URL:
https://www.aclweb.org/anthology/P17-1099
DOI:
10.18653/v1/P17-1099
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PDF:
http://aclanthology.lst.uni-saarland.de/P17-1099.pdf
Presentation:
 P17-1099.Presentation.pdf
Note:
 P17-1099.Notes.pdf
Video:
 https://vimeo.com/234956256