Papers › Ranking Sentences for Extractive Summarization with Reinforcement Learning

Ranking Sentences for Extractive Summarization with Reinforcement Learning

23 Feb 2018NAACL 2018 6arXiv:1802.08636archive 2025-07-28

Shashi Narayan, Shay B. Cohen, Mirella Lapata

Single document summarization is the task of producing a shorter version of a document while preserving its principal information content. In this paper we conceptualize extractive summarization as a sentence ranking task and propose a novel training algorithm which globally optimizes the ROUGE evaluation metric through a reinforcement learning objective. We use our algorithm to train a neural summarization model on the CNN and DailyMail datasets and demonstrate experimentally that it outperforms state-of-the-art extractive and abstractive systems when evaluated automatically and by humans.

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