FEQA: A Question Answering Evaluation Framework for Faithfulness Assessment in Abstractive Summarization

ACL 2020 Esin DurmusHe HeMona Diab

Neural abstractive summarization models are prone to generate content inconsistent with the source document, i.e. unfaithful. Existing automatic metrics do not capture such mistakes effectively... (read more)

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