Extractive Summarization as Text Matching

ACL, pp. 6197-6208, 2020.

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We propose a Siamese-BERT architecture to compute the similarity between the source document and the candidate summary

Abstract:

This paper creates a paradigm shift with regard to the way we build neural extractive summarization systems. Instead of following the commonly used framework of extracting sentences individually and modeling the relationship between sentences, we formulate the extractive summarization task as a semantic text matching problem, in which a...More
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