Papers with summary factuality

1 papers
Improving Factuality of Abstractive Summarization without Sacrificing Summary Quality (2023.acl-short)

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Challenge: Recent studies have shown that most abstractive summarization models are unfaithful and suffer from a wide range of hallucination.
Approach: They propose a candidate summary generation and ranking technique to improve summary factuality without sacrificing quality.
Outcome: The proposed method shows that the model trained using the proposed method improves on factuality and similarity-based metrics without conflicting with the model.

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