@InProceedings{ma-deng-yang:2016:COLING,
  author    = {Ma, Shulei  and  Deng, Zhi-Hong  and  Yang, Yunlun},
  title     = {An Unsupervised Multi-Document Summarization Framework Based on Neural Document Model},
  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     = {1514--1523},
  abstract  = {In the age of information exploding, multi-document summarization is attracting
	particular attention for the ability to help people get the main ideas in a
	short time. Traditional extractive methods simply treat the document set as a
	group of sentences while ignoring the global semantics of the documents.
	Meanwhile, neural document model is effective on representing the semantic
	content of documents in low-dimensional vectors. In this paper, we propose a
	document-level reconstruction framework named DocRebuild, which reconstructs
	the documents with summary sentences through a neural document model and
	selects summary sentences to minimize the reconstruction error. We also apply
	two strategies, sentence filtering and beamsearch, to improve the performance
	of our method. Experimental results on the benchmark datasets DUC 2006 and
	DUC 2007 show that DocRebuild is effective and outperforms the state-of-the-art
	unsupervised algorithms.},
  url       = {http://aclweb.org/anthology/C16-1143}
}