Enhancing Factual Consistency of Abstractive Summarization

• URL: http://arxiv.org/abs/2003.08612v8
• Date: Mon, 15 Mar 2021 00:40:15 GMT
• Title: Enhancing Factual Consistency of Abstractive Summarization
• Authors: Chenguang Zhu, William Hinthorn, Ruochen Xu, Qingkai Zeng, Michael Zeng, Xuedong Huang, Meng Jiang
• Abstract summary: We propose a fact-aware summarization model FASum to extract and integrate factual relations into the summary generation process. We then design a factual corrector model FC to automatically correct factual errors from summaries generated by existing systems.
• Score: 57.67609672082137
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Automatic abstractive summaries are found to often distort or fabricate facts in the article. This inconsistency between summary and original text has seriously impacted its applicability. We propose a fact-aware summarization model FASum to extract and integrate factual relations into the summary generation process via graph attention. We then design a factual corrector model FC to automatically correct factual errors from summaries generated by existing systems. Empirical results show that the fact-aware summarization can produce abstractive summaries with higher factual consistency compared with existing systems, and the correction model improves the factual consistency of given summaries via modifying only a few keywords.

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