Improving Factual Consistency of Abstractive Summarization via Question Answering

Feng Nan, Cicero Nogueira dos Santos, Henghui Zhu, Patrick Ng, Kathleen McKeown, Ramesh Nallapati, Dejiao Zhang, Zhiguo Wang, Andrew O. Arnold, Bing Xiang


Abstract
A commonly observed problem with the state-of-the art abstractive summarization models is that the generated summaries can be factually inconsistent with the input documents. The fact that automatic summarization may produce plausible-sounding yet inaccurate summaries is a major concern that limits its wide application. In this paper we present an approach to address factual consistency in summarization. We first propose an efficient automatic evaluation metric to measure factual consistency; next, we propose a novel learning algorithm that maximizes the proposed metric during model training. Through extensive experiments, we confirm that our method is effective in improving factual consistency and even overall quality of the summaries, as judged by both automatic metrics and human evaluation.
Anthology ID:
2021.acl-long.536
Volume:
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)
Month:
August
Year:
2021
Address:
Online
Venues:
ACL | IJCNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
6881–6894
Language:
URL:
https://aclanthology.org/2021.acl-long.536
DOI:
10.18653/v1/2021.acl-long.536
Bibkey:
Cite (ACL):
Feng Nan, Cicero Nogueira dos Santos, Henghui Zhu, Patrick Ng, Kathleen McKeown, Ramesh Nallapati, Dejiao Zhang, Zhiguo Wang, Andrew O. Arnold, and Bing Xiang. 2021. Improving Factual Consistency of Abstractive Summarization via Question Answering. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 6881–6894, Online. Association for Computational Linguistics.
Cite (Informal):
Improving Factual Consistency of Abstractive Summarization via Question Answering (Nan et al., ACL 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.acl-long.536.pdf
Video:
 https://aclanthology.org/2021.acl-long.536.mp4
Data
CNN/Daily Mail