@InProceedings{celikyilmaz-EtAl:2018:N18-1,
  author    = {Celikyilmaz, Asli  and  Bosselut, Antoine  and  He, Xiaodong  and  Choi, Yejin},
  title     = {Deep Communicating Agents for Abstractive Summarization},
  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     = {1662--1675},
  abstract  = {We present deep communicating agents in an encoder-decoder architecture to address the challenges of representing a long document for abstractive summarization. With deep communicating agents, the task of encoding a long text is divided across multiple collaborating agents, each in charge of a subsection of the input text. These encoders are connected to a single decoder, trained end-to-end using reinforcement learning to generate a focused and coherent summary. Empirical results demonstrate that multiple communicating encoders lead to a higher quality summary compared to several strong baselines, including those based on a single encoder or multiple non-communicating encoders.},
  url       = {http://www.aclweb.org/anthology/N18-1150}
}