Ranking Sentences for Extractive Summarization with Reinforcement Learning

Shashi Narayan; Shay B. Cohen; Mirella Lapata

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arXiv · 1802.08636

Ranking Sentences for Extractive Summarization with Reinforcement Learning

Shashi Narayan·Shay B. Cohen·Mirella Lapata

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Abstract

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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BibTeX RIS

Shashi Narayan, Shay B. Cohen, Mirella Lapata. 2018-02-23. Ranking Sentences for Extractive Summarization with Reinforcement Learning. https://arxiv.org/abs/1802.08636

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