@InProceedings{nema-EtAl:2017:Long,
  author    = {Nema, Preksha  and  Khapra, Mitesh M.  and  Laha, Anirban  and  Ravindran, Balaraman},
  title     = {Diversity driven attention model for query-based abstractive summarization},
  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     = {1063--1072},
  abstract  = {Abstractive summarization aims to generate a shorter version of the document
	covering all the salient points in a compact and coherent fashion. On the other
	hand, query-based summarization highlights those points that are relevant in
	the context of a given query. The encode-attend-decode paradigm has achieved
	notable success in machine translation, extractive summarization, dialog
	systems, etc. But it suffers from the drawback of generation of repeated
	phrases. In this work we propose a model for the query-based summarization task
	based on the encode-attend-decode paradigm with two key additions (i) a query
	attention model (in addition to document attention model) which learns to focus
	on different portions of the query at different time steps (instead of using a
	static representation for the query) and (ii) a new diversity based attention
	model which aims to alleviate the problem of repeating phrases in the summary.
	In order to enable the testing of this model we introduce a new query-based
	summarization dataset building on debatepedia. Our experiments show that with
	these two additions the proposed model clearly outperforms vanilla
	encode-attend-decode models with a gain of 28\% (absolute) in ROUGE-L scores.},
  url       = {http://aclweb.org/anthology/P17-1098}
}