@InProceedings{peyrard-ecklekohler:2016:COLING,
  author    = {Peyrard, Maxime  and  Eckle-Kohler, Judith},
  title     = {A General Optimization Framework for Multi-Document Summarization Using Genetic Algorithms and Swarm Intelligence},
  booktitle = {Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers},
  month     = {December},
  year      = {2016},
  address   = {Osaka, Japan},
  publisher = {The COLING 2016 Organizing Committee},
  pages     = {247--257},
  abstract  = {Extracting summaries via integer linear programming and submodularity are
	popular and successful techniques in extractive multi-document summarization.
	However, many interesting optimization objectives are neither submodular nor
	factorizable into an integer linear program. We address this issue and present
	a general optimization framework where any function of input documents and a
	system summary can be plugged in. Our framework includes two kinds of
	summarizers -- one based on genetic algorithms, the other using a swarm
	intelligence approach. In our experimental evaluation, we investigate the
	optimization of two information-theoretic summary evaluation metrics and find
	that our framework yields competitive results compared to several strong
	summarization baselines. Our comparative analysis of the genetic and swarm
	summarizers reveals interesting complementary properties.},
  url       = {http://aclweb.org/anthology/C16-1024}
}