Automatic Personalized Summarization Using Non-negative Matrix Factorization and Relevance Measure

In this paper, a new automatic personalized summarization method, which uses non-negative matrix factorization (NMF) and relevance measure (RM), is introduced to extract meaningful sentences from to retrieve documents in Internet. The proposed method can improve the quality of personalized summaries because the inherent semantics of the documents are well reflected by using the semantic features calculated by NMF and the sentences most relevant to the given query are extracted efficiently by using the semantic variables derived by NMF. Besides, it uses RM to summarize generic summary so that it can select sentences covering the major topics of the document. The experimental results using Yahoo-Korea News data show that the proposed method achieves better performance than the other methods.