Personalized Summarization Agent Using Non-negative Matrix Factorization

Authors:
Sun Park
Affiliations:
Department of Computer Engineering, Honam University, Gwangju, Korea
Venue:
PRICAI '08 Proceedings of the 10th Pacific Rim International Conference on Artificial Intelligence: Trends in Artificial Intelligence
Year:
2008

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Abstract

This paper proposes personalized summarization agent using non-negative matrix factorization (NMF) to extract sentences relevant to a user interesting for a generic and query based summary. The proposed agent uses NMF to summarize generic summary so that it can extract sentences covering the major topics of the search results with respect to user interesting. Besides, it can improve the quality of query based summaries because the inherent semantics of the search results are well reflected by using NMF and the sentences most relevant to the given query. The experimental results demonstrate that the proposed method achieves better performance the other methods.