@InProceedings{chali-tanvee-nayeem:2017:I17-2,
  author    = {Chali, Yllias  and  Tanvee, Moin  and  Nayeem, Mir Tafseer},
  title     = {Towards Abstractive Multi-Document Summarization Using Submodular Function-Based Framework, Sentence Compression and Merging},
  booktitle = {Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)},
  month     = {November},
  year      = {2017},
  address   = {Taipei, Taiwan},
  publisher = {Asian Federation of Natural Language Processing},
  pages     = {418--424},
  abstract  = {We propose a submodular function-based summarization system which integrates
	three important measures namely importance, coverage, and non-redundancy to
	detect the important sentences for the summary. We design monotone and
	submodular functions which allow us to apply an efficient and scalable greedy
	algorithm to obtain informative and well-covered summaries. In addition, we
	integrate two abstraction-based methods namely sentence compression and merging
	for generating an abstractive sentence set. We design our summarization models
	for both generic and query-focused summarization. Experimental results on
	DUC-2004 and DUC-2007 datasets show that our generic and query-focused
	summarizers have outperformed the state-of-the-art summarization systems in
	terms of ROUGE-1 and ROUGE-2 recall and F-measure.},
  url       = {http://www.aclweb.org/anthology/I17-2071}
}