The Journal of Machine Learning Research
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
Using random walks for question-focused sentence retrieval
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Automatic summarising: The state of the art
Information Processing and Management: an International Journal
Information Processing and Management: an International Journal
Latent dirichlet allocation based multi-document summarization
Proceedings of the second workshop on Analytics for noisy unstructured text data
Query-Focused Summarization by Combining Topic Model and Affinity Propagation
APWeb/WAIM '09 Proceedings of the Joint International Conferences on Advances in Data and Web Management
Latent Dirichlet learning for document summarization
ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing
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
Multi-document summarization using sentence-based topic models
ACLShort '09 Proceedings of the ACL-IJCNLP 2009 Conference Short Papers
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In this paper, we propose a novel update summarization framework based on topic correlation analysis. The topics are first extracted from the two document sets provided in the task of update summarization by means of Latent Dirichlet Allocation (LDA) topic model. Then, the correlation between the new topics and the old topics are identified, based on which we further defined four categories of topic evolution patterns to capture the topic shift between the two document collections. We develop a new sentence ranking algorithm, i.e. CorrRank, which fully incorporates the topic evolution in the process of sentence ranking and sentence selection in update summarization. We choose the DUC 2008 and 2009 query-oriented multi-document update summarization tasks to examine the proposed model. Experimental results show the effectiveness of the LDA topic correlation analysis based update summarization framework.