@InProceedings{pvs-meyer:2017:Long,
  author    = {PVS, Avinesh  and  Meyer, Christian M.},
  title     = {Joint Optimization of User-desired Content in Multi-document Summaries by Learning from User Feedback},
  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     = {1353--1363},
  abstract  = {In this paper, we propose an extractive multi-document summarization (MDS)
	system using joint optimization and active learning for content selection
	grounded in user feedback. Our method interactively obtains user feedback to
	gradually improve the results of a state-of-the-art integer linear programming
	(ILP) framework for MDS. Our methods complement fully automatic methods in
	producing high-quality summaries with a minimum number of iterations and
	feedbacks.
	We conduct multiple simulation-based experiments and analyze the effect of
	feedback-based concept selection in the ILP setup in order to maximize the
	user-desired content in the summary.},
  url       = {http://aclweb.org/anthology/P17-1124}
}