Probabilistic latent semantic indexing
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
The Journal of Machine Learning Research
The automated acquisition of topic signatures for text summarization
COLING '00 Proceedings of the 18th conference on Computational linguistics - Volume 1
A graph-theoretic approach to extract storylines from search results
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Probabilistic author-topic models for information discovery
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Automatic evaluation of summaries using N-gram co-occurrence statistics
NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1
Topic themes for multi-document summarization
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
LDA-based document models for ad-hoc retrieval
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Bayesian query-focused summarization
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Topic sentiment mixture: modeling facets and opinions in weblogs
Proceedings of the 16th international conference on World Wide Web
Topic-focused multi-document summarization using an approximate oracle score
COLING-ACL '06 Proceedings of the COLING/ACL on Main conference poster sessions
ArnetMiner: extraction and mining of academic social networks
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Multi-document summarization by maximizing informative content-words
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
CorrRank: update summarization based on topic correlation analysis
ICIC'10 Proceedings of the Advanced intelligent computing theories and applications, and 6th international conference on Intelligent computing
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The goal of query-focused summarization is to extract a summary for a given query from the document collection. Although much work has been done for this problem, there are still many challenging issues: (1) The length of the summary is predefined by, for example, the number of word tokens or the number of sentences. (2) A query usually asks for information of several perspectives (topics); however existing methods cannot capture topical aspects with respect to the query. In this paper, we propose a novel approach by combining statistical topic model and affinity propagation. Specifically, the topic model, called qLDA, can simultaneously model documents and the query. Moreover, the affinity propagation can automatically discover key sentences from the document collection without predefining the length of the summary. Experimental results on DUC05 and DUC06 data sets show that our approach is effective and the summarization performance is better than baseline methods.