Automatic query-based personalized summarization that uses pseudo relevance feedback with NMF

  • Authors:
  • Sun Park;Dong Un An

  • Affiliations:
  • Chonbuk National University, South Korea;Chonbuk National University, South Korea

  • Venue:
  • Proceedings of the 4th International Conference on Uniquitous Information Management and Communication
  • Year:
  • 2010

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Abstract

This paper proposes a new automatic query-based personalized summarization using Pseudo Relevance Feedback (PRF) with Non-negative Matrix Factorization (NMF) 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 PRF with NMF. Besides, it can reduce the semantic gap between the low level search result and high level user's perception by means of PRF with semantic features. The experimental results demonstrate that the proposed method achieves better performance than the other methods.