Enhancing Factual Consistency of Abstractive Summarization
122 Citations•2021•
Chenguang Zhu, William Hinthorn, Ruochen Xu
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A fact-aware summarization model FASum is proposed to extract and integrate factual relations into the summary generation process via graph attention and a factual corrector model FC is designed to automatically correct factual errors from summaries generated by existing systems.
Abstract
Chenguang Zhu, William Hinthorn, Ruochen Xu, Qingkai Zeng, Michael Zeng, Xuedong Huang, Meng Jiang. Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 2021.