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ISQA: Informative Factuality Feedback for Scientific Summarization
April 23, 2024, 4:49 a.m. | Zekai Li, Yanxia Qin, Qian Liu, Min-Yen Kan
cs.CL updates on arXiv.org arxiv.org
Abstract: We propose Iterative Facuality Refining on Informative Scientific Question-Answering (ISQA) feedback\footnote{Code is available at \url{https://github.com/lizekai-richard/isqa}}, a method following human learning theories that employs model-generated feedback consisting of both positive and negative information. Through iterative refining of summaries, it probes for the underlying rationale of statements to enhance the factuality of scientific summarization. ISQA does this in a fine-grained manner by asking a summarization agent to reinforce validated statements in positive feedback and fix incorrect ones …
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