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Improving Factual Consistency of Abstractive Summarization via Question Answering

Authors :
Nan, Feng
Santos, Cicero Nogueira dos
Zhu, Henghui
Ng, Patrick
McKeown, Kathleen
Nallapati, Ramesh
Zhang, Dejiao
Wang, Zhiguo
Arnold, Andrew O.
Xiang, Bing
Publication Year :
2021

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.<br />Comment: ACL-IJCNLP 2021

Details

Database :
arXiv
Publication Type :
Report
Accession number :
edsarx.2105.04623
Document Type :
Working Paper