  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.
