Generic multi-document summarization using cluster refinement and NMF

In this paper, a generic summarization method that uses cluster refinement and NMF is introduced to extract meaningful sentences from documents. The proposed method uses cluster refinement to improve the quality of document clustering since it helps us to remove dissimilarity information easily and avoid biased inherent semantics of documents to be reflected in clusters by NMF. In addition, it uses the weighted semantic variable to select meaningful sentences because the extracted sentences are well covered with the major topics of document. The experimental results demonstrate that the proposed method has better performance than other methods that use the other methods.