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.
@article{arxiv.2003.08612,
title = {Enhancing Factual Consistency of Abstractive Summarization},
author = {Chenguang Zhu and William Hinthorn and Ruochen Xu and Qingkai Zeng and Michael Zeng and Xuedong Huang and Meng Jiang},
journal= {arXiv preprint arXiv:2003.08612},
year = {2021}
}
Comments
Prediction results available at: https://github.com/zcgzcgzcg1/FASum/