Automatic Multi-document Summarization Based on Clustering and Nonnegative Matrix Factorization

In this paper, a novel summarization method that uses nonnegative matrix factorization (NMF) and the -clus--tering method is introduced to extract meaningful sentences relevant to a given query. The proposed method decomposes a sentence into the linear combination of sparse nonnegative semantic features so that it can represent a sentence as the sum of a few semantic features that are comprehensible intuitively. It can improve the quality of document summaries because it can avoid extracting those sentences whose similarities with the query are high but that are meaningless by using the similarity between the query and the semantic features. In addition, the proposed approach uses the clustering method to remove noise and avoid the biased inherent semantics of the documents being reflected in summaries. The method can -ensure the coherence of summaries by using the rank score of sentences with respect to semantic -features. The experimental results demonstrate that the proposed method has better performance than other -methods that use the thesaurus, the latent semantic analysis (LSA), the K-means, and the NMF.