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This paper introduces a novel framework for extractive summarization called M ATCH S UM, which formulates the task as a semantic text matching problem rather than extracting sentences individually. The proposed approach demonstrates superior performance on benchmark datasets, achieving state-of-the-art results on CNN/DailyMail with a ROUGE-1 score of 44.41. The authors provide a comprehensive analysis of the differences between sentence-level and summary-level extractors, highlighting the advantages of thei

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data science

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EN