Document Summarization Using Non-Negative Matrix Factorization and Relevance Feedback

This paper proposes a new document summarization method using relevance feedback (RF) and non-negative matrix factorization (NMF) to distill the contents of the documents with respect to a given query. The proposed method expands the query through relevance feedback to reflect user´s requirement and extract meaningful sentences using the cosine similarity measure between the expanded query and the semantic features which are obtained by NMF. It can reduce the semantic gap between the low level feature representation in vector model and the high level user´s perception by means of iterative relevance feedback. The experimental results demonstrate that the proposed method achieves better performance than the other methods.