Improving Factual Consistency in Summarization with Compression-Based Post-Editing

State-of-the-art summarization models still struggle to be factually consistent with the input text. A model-agnostic way to address this problem is post-editing the generated summaries. However, existing approaches typically fail to remove entity errors if a suitable input entity replacement is not available or may insert erroneous content. In our work, we focus on removing extrinsic entity errors, or entities not in the source, to improve consistency while retaining the summary's essential information and form. We propose to use sentence-compression data to train the post-editing model to take a summary with extrinsic entity errors marked with special tokens and output a compressed, well-formed summary with those errors removed. We show that this model improves factual consistency while maintaining ROUGE, improving entity precision by up to 30% on XSum, and that this model can be applied on top of another post-editor, improving entity precision by up to a total of 38%. We perform an extensive comparison of post-editing approaches that demonstrate trade-offs between factual consistency, informativeness, and grammaticality, and we analyze settings where posteditors show the largest improvements. Source: The oil price collapse sent global markets reeling throughout 2015. ... Brent crude oil was up 3% at $37.60 per barrel for the day but down 35$28 a barrel for much of the year. Entity Swap No Change Rewrite Wall Street markets closed lower on the last trading day of 2015 as oil prices languished at $37.60 per barrel ... but remain down 35% over the year. Delete Wall Street markets closed lower on 2015 as oil prices languished at a for much of the year. Compress (ours) Wall Street markets closed lower as oil prices languished for much of the year.