Automatic Personalized Summarization Using Non-negative Matrix Factorization and Relevance Measure

Authors:
Sun Park;Ju-Hong Lee;Jae-Won Song
Affiliations:
-;-;-
Venue:
IWSCA '08 Proceedings of the 2008 IEEE International Workshop on Semantic Computing and Applications
Year:
2008

Citing 0
Cited 3

Personalized Document Summarization Using Non-negative Semantic Feature and Non-negative Semantic Variable

IDEAL '08 Proceedings of the 9th International Conference on Intelligent Data Engineering and Automated Learning
Automatic personalized text summarization agent using generic relevance weight based on NMF

ICOIN'09 Proceedings of the 23rd international conference on Information Networking
Automatic query-based personalized summarization that uses pseudo relevance feedback with NMF

Proceedings of the 4th International Conference on Uniquitous Information Management and Communication

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Abstract

In this paper, a new automatic personalized summarization method, which uses non-negative matrix factorization (NMF) and Relevance Measure (RM), is introduced 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 uses RM to summarize generic summary so that it can select sentences covering the major topics of the document. The experimental results using Yahoo-Korea News data show that the proposed method achieves better performance than the other methods.