An Entity-Driven Framework for Abstractive Summarization

Eva Sharma, Luyang Huang, Zhe Hu, Lu Wang

Abstract
Abstractive summarization systems aim to produce more coherent and concise summaries than their extractive counterparts. Popular neural models have achieved impressive results for single-document summarization, yet their outputs are often incoherent and unfaithful to the input. In this paper, we introduce SENECA, a novel System for ENtity-drivEn Coherent Abstractive summarization framework that leverages entity information to generate informative and coherent abstracts. Our framework takes a two-step approach: (1) an entity-aware content selection module first identifies salient sentences from the input, then (2) an abstract generation module conducts cross-sentence information compression and abstraction to generate the final summary, which is trained with rewards to promote coherence, conciseness, and clarity. The two components are further connected using reinforcement learning. Automatic evaluation shows that our model significantly outperforms previous state-of-the-art based on ROUGE and our proposed coherence measures on New York Times and CNN/Daily Mail datasets. Human judges further rate our system summaries as more informative and coherent than those by popular summarization models.
Anthology ID:
D19-1323
Volume:
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
Month:
November
Year:
2019
Address:
Hong Kong, China
Editors:
Kentaro Inui, Jing Jiang, Vincent Ng, Xiaojun Wan
Venues:
EMNLP | IJCNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
3280–3291
Language:
URL:
https://aclanthology.org/D19-1323
DOI:
10.18653/v1/D19-1323
Bibkey:
Cite (ACL):
Eva Sharma, Luyang Huang, Zhe Hu, and Lu Wang. 2019. An Entity-Driven Framework for Abstractive Summarization. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 3280–3291, Hong Kong, China. Association for Computational Linguistics.
Cite (Informal):
An Entity-Driven Framework for Abstractive Summarization (Sharma et al., EMNLP-IJCNLP 2019)
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PDF:
https://preview.aclanthology.org/emnlp22-frontmatter/D19-1323.pdf
Attachment:
 D19-1323.Attachment.zip
Data
CNN/Daily Mail, New York Times Annotated Corpus
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