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

Understand

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.