@InProceedings{narayan-cohen-lapata:2018:N18-1,
  author    = {Narayan, Shashi  and  Cohen, Shay B.  and  Lapata, Mirella},
  title     = {Ranking Sentences for Extractive Summarization with Reinforcement Learning},
  booktitle = {Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)},
  month     = {June},
  year      = {2018},
  address   = {New Orleans, Louisiana},
  publisher = {Association for Computational Linguistics},
  pages     = {1747--1759},
  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.},
  url       = {http://www.aclweb.org/anthology/N18-1158}
}