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. © Springer-Verlag Berlin Heidelberg 2006.
CITATION STYLE
Park, S., Lee, J. H., Ahn, C. M., Hong, J. S., & Chun, S. J. (2006). Query based summarization using non-negative matrix factorization. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4253 LNAI-III, pp. 84–89). Springer Verlag. https://doi.org/10.1007/11893011_11
Mendeley helps you to discover research relevant for your work.