Personalized Document Summarization Using Non-negative Semantic Feature and Non-negative Semantic Variable

Recently, the necessity of personalized document summarization reflecting user interest from search results is increased. This paper proposes a personalized document summarization method using non-negative semantic feature (NSF) and non-negative semantic variable (NSV) to extract sentences relevant to a user interesting. The proposed method uses NSV to summarize generic summary so that it can extract sentences covering the major topics of the document with respect to user interesting. Besides, it can improve the quality of personalized summaries because the inherent semantics of the documents are well reflected by using NSF and the sentences most relevant to the given query are extracted efficiently by using NSV. The experimental results demonstrate that the proposed method achieves better performance the other methods.