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. 1
In another approach, REF use a reinforcement learning method to optimize the Rouge evaluation metric for text summarization.
Ranking Sentences for Extractive Summarization with Reinforcement Learning
ArXiv
ArXiv
Computer Science