@InProceedings{see-liu-manning:2017:Long,
  author    = {See, Abigail  and  Liu, Peter J.  and  Manning, Christopher D.},
  title     = {Get To The Point: Summarization with Pointer-Generator Networks},
  booktitle = {Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  month     = {July},
  year      = {2017},
  address   = {Vancouver, Canada},
  publisher = {Association for Computational Linguistics},
  pages     = {1073--1083},
  abstract  = {Neural sequence-to-sequence models have provided a viable new approach for
	abstractive text summarization (meaning they are not restricted to simply
	selecting and rearranging passages from the original text). However, these
	models have two shortcomings: they are liable to reproduce factual details
	inaccurately, and they tend to repeat themselves. In this work we propose a
	novel architecture that augments the standard sequence-to-sequence attentional
	model in two orthogonal ways. First, we use a hybrid pointer-generator network
	that can copy words from the source text via pointing, which aids accurate
	reproduction of information, while retaining the ability to produce novel words
	through the generator. Second, we use coverage to keep track of what has been
	summarized, which discourages repetition. We apply our model to the CNN / Daily
	Mail summarization task, outperforming the current abstractive state-of-the-art
	by at least 2 ROUGE points.},
  url       = {http://aclweb.org/anthology/P17-1099}
}