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
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
Document clustering based on non-negative matrix factorization
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Identifying similarities, periodicities and bursts for online search queries
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
TSCAN: a novel method for topic summarization and content anatomy
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Automatic generic document summarization based on non-negative matrix factorization
Information Processing and Management: an International Journal
Manifold-ranking based topic-focused multi-document summarization
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
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In this paper, we study topic decomposition and summarization for a temporal-sequenced text corpus of a specific topic. The task is to discover different topic aspects (i.e., sub-topics) and incidents related to each sub-topic of the text corpus, and generate summaries for them. We present a solution with the following steps: (1) deriving sub-topics by applying Non-negative Matrix Factorization (NMF) to terms-by-sentences matrix of the text corpus; (2) detecting incidents of each sub-topic and generating summaries for both sub-topic and its incidents by examining the constitution of its encoding vector generated by NMF; (3) ranking each sentences based on the encoding matrix and selecting top ranked sentences of each sub-topic as the text corpus' summary. Experimental results show that the proposed topic decomposition method can effectively detect various aspects of original documents. Besides, the topic summarization method achieves better results than some well-studied methods.