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Topic-based Multi-document Summarization Using Non-negative Matrix Factorization and K-means  

Park, Sun (호남대학교 컴퓨터공학과)
Lee, Ju-Hong (인하대학교 컴퓨터 정보공학과)
Publication Information
Journal of KIISE:Software and Applications / v.35, no.4, 2008 , pp. 255-264 More about this Journal
Abstract
This paper proposes a novel method using K-means and Non-negative matrix factorization (NMF) for topic -based multi-document summarization. NMF decomposes weighted term by sentence matrix into two sparse non-negative matrices: semantic feature matrix and semantic variable matrix. Obtained semantic features are comprehensible intuitively. Weighted similarity between topic and semantic features can prevent meaningless sentences that are similar to a topic from being selected. K-means clustering removes noises from sentences so that biased semantics of documents are not reflected to summaries. Besides, coherence of document summaries can be enhanced by arranging selected sentences in the order of their ranks. The experimental results show that the proposed method achieves better performance than other methods.
Keywords
multi-document summarization; non-negative matrix factorization; clustering; topic-based summarization; weighted similarity;
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