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

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 nonfactual 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 nonfactual 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.