Papers with summary-quality estimation

1 papers
HaRiM+: Evaluating Summary Quality with Hallucination Risk (2022.aacl-main)

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Challenge: Existing summarization models are limited in measuring the factual inconsistency of generated summaries.
Approach: They propose a decoder overconfidence-regularizing objective as a hallucination risk measurement to better estimate the quality of generated summaries.
Outcome: The proposed metric is reference-free and requires no training or modules . it records state-of-the-art correlation to human judgment on three sets of summary-quality annotations.

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