The use of MMR, diversity-based reranking for reordering documents and producing summaries
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
The anatomy of a large-scale hypertextual Web search engine
WWW7 Proceedings of the seventh international conference on World Wide Web 7
Authoritative sources in a hyperlinked environment
Proceedings of the ninth annual ACM-SIAM symposium on Discrete algorithms
New Methods in Automatic Extracting
Journal of the ACM (JACM)
Generic text summarization using relevance measure and latent semantic analysis
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
The Journal of Machine Learning Research
Active learning via transductive experimental design
ICML '06 Proceedings of the 23rd international conference on Machine learning
Latent dirichlet allocation based multi-document summarization
Proceedings of the second workshop on Analytics for noisy unstructured text data
Automatic generic document summarization based on non-negative matrix factorization
Information Processing and Management: an International Journal
Latent Dirichlet Allocation and Singular Value Decomposition Based Multi-document Summarization
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
Exploring content models for multi-document summarization
NAACL '09 Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics
LexRank: graph-based lexical centrality as salience in text summarization
Journal of Artificial Intelligence Research
The automatic creation of literature abstracts
IBM Journal of Research and Development
A study of global inference algorithms in multi-document summarization
ECIR'07 Proceedings of the 29th European conference on IR research
Multi-document summarization via budgeted maximization of submodular functions
HLT '10 Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Graph Regularized Nonnegative Matrix Factorization for Data Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Probabilistic latent semantic analysis
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Multiple aspect summarization using integer linear programming
EMNLP-CoNLL '12 Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning
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Multi-document summarization attempts to select the most important information to generate a compressed summary from a collection of documents. From the perspective of data reconstruction, a good summary may also well reconstruct the original documents. A document generally contains a variety of information centered around a main topic and covers different aspects of the main topic. In this paper we propose a novel model that combines data reconstruction and topic decomposition to summarize the documents, named TopicDSDR, which can not only best reconstruct the original documents but also capture the semantic similarity and main topics. We discuss two kinds of reconstructions: linear reconstruction and nonnegative reconstruction. We use the generalized Kullback-Leibler(KL) divergence as the loss function to evaluate the quality of summary for linear and nonnegative reconstruction and develop two new algorithms respectively. We conduct experiments on DUC2006 and DUC2007 summarization data sets, the experimental results demonstrate the effectiveness of our proposed methods.