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This paper proposes a new query based personalized summarization agent using non-negative matrix factorization (NMF) and relevance feedback (RF) 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 can reduce the semantic gap between the low level search result and high level userpsilas perception by means of iterative RF. The experimental results demonstrate that the proposed method achieves better performance than the other methods.