@InProceedings{jkurisinkel-zhang-varma:2017:I17-1,
  author    = {J Kurisinkel, Litton  and  Zhang, Yue  and  Varma, Vasudeva},
  title     = {Abstractive Multi-document Summarization by Partial Tree Extraction, Recombination and Linearization},
  booktitle = {Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)},
  month     = {November},
  year      = {2017},
  address   = {Taipei, Taiwan},
  publisher = {Asian Federation of Natural Language Processing},
  pages     = {812--821},
  abstract  = {Existing work for abstractive multidocument summarization utilise existing
	phrase structures directly extracted from input documents to generate summary
	sentences. These methods can suffer from lack of consistence and coherence in
	merging phrases. We introduce a novel approach for abstractive multidocument
	summarization through partial dependency tree extraction, recombination and
	linearization. The method entrusts the summarizer to generate its own topically
	coherent sequential structures from scratch for effective communication.
	Results on TAC 2011, DUC-2004 and 2005 show that our system gives competitive
	results compared with state of the art abstractive summarization approaches in
	the literature. We also achieve competitive results  in linguistic quality
	assessed by human evaluators.},
  url       = {http://www.aclweb.org/anthology/I17-1082}
}