Evaluating the Factual Consistency of Abstractive Text Summarization
factCC Evaluating the Factual Consistency of Abstractive Text SummarizationAuthors: Wojciech Kryściński, Bryan McCann, Caiming Xiong, and Richard Socher Introduction Currently used metrics for assessing summarization algorithms do not account for whether summaries are factually consistent with source documents.We propose a weakly-supervised, model-based approach for verifying factual consistency and identifying conflicts between source documents and a generated summary.Training data is generated by applying a series of rule-based transformations to the sentences of source documents.The factual consistency model is then trained jointly […]
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