Abstractive summarization models often generate inconsistent summaries
containing factual errors or hallucinated content. Recent works focus on
correcting factual errors in generated summaries via post-editing. Such
correction models are trained using adversarial non-factual summaries
constructed using heuristic rules for injecting errors. However, generating
non-factual summaries using heuristics often does not generalize well to actual
model errors. In this work, we propose to generate hard, representative
synthetic examples of non-factual summaries through infilling language models.
With this data, we train a more robust fact-correction model to post-edit the
summaries to improve factual consistency. Through quantitative and qualitative
experiments on two popular summarization datasets -- CNN/DM and XSum -- we show
that our approach vastly outperforms prior methods in correcting erroneous
summaries. Our model -- FactEdit -- improves factuality scores by over ~11
points on CNN/DM and over ~31 points on XSum on average across multiple
summarization models, producing more factual summaries while maintaining
competitive summarization quality.

本文提出使用语言模型生产更加具有代表性的无事实描述样例，训练出更加强大和鲁棒的基于事实更正的模型对生成的摘要进行错误更正提高摘要的事实一致性。