Query based summarization using non-negative matrix factorization

Authors:
Sun Park;Ju-Hong Lee;Chan-Min Ahn;Jun Sik Hong;Seok-Ju Chun
Affiliations:
Department of Computer Science and Information Engineering, Inha University, Korea;Department of Computer Science and Information Engineering, Inha University, Korea;Department of Computer Science and Information Engineering, Inha University, Korea;Department of Electronic Engineering, Youngdong University, Korea;Department of Computer Education, Seoul National University of Education, Korea
Venue:
KES'06 Proceedings of the 10th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part III
Year:
2006

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Abstract

Query based document summaries are important in document retrieval system to show the concise relevance of documents retrieved to a query. This paper proposes a novel method using the Non-negative Matrix Factorization (NMF) to extract the query relevant sentences from documents for query based summaries. The proposed method doesn't need the training phase using training data comprising queries and query specific documents. And it exactly summarizes documents for the given query by using semantic features and semantic variables without complex processing like transformation of documents to graphs because the NMF have a great power to naturally extract semantic features representing the inherent structure of a document.