Correcting Diverse Factual Errors in Abstractive Summarization via Post-Editing and Language Model Infilling

• URL: http://arxiv.org/abs/2210.12378v1
• Date: Sat, 22 Oct 2022 07:16:19 GMT
• Title: Correcting Diverse Factual Errors in Abstractive Summarization via Post-Editing and Language Model Infilling
• Authors: Vidhisha Balachandran, Hannaneh Hajishirzi, William Cohen, Yulia Tsvetkov
• Abstract summary: 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.
• Score: 56.70682379371534
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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