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

TL;DR

This paper introduces a compression-based post-editing method to enhance factual consistency in summarization by removing extrinsic entity errors, achieving significant improvements without sacrificing summary quality.

Contribution

It proposes a novel training approach using sentence compression data to effectively remove extrinsic entity errors in summaries, improving factual accuracy.

Findings

Entity precision improved by up to 30% on XSum.

Combining with other post-editors increases precision by 38%.

Model maintains ROUGE scores while enhancing factual consistency.

Abstract

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 post-editors show the largest improvements.