Name
Title | ||
|---|---|---|
Query-Based Multi-Document Summarization Using Non-Negative Semantic Feature and NMF Clustering |
Abstract | ||
|---|---|---|
In this paper, a novel summarization method, which uses non-negative matrix factorization (NMF) and NMF clustering, is introduced to extract meaningful sentences from query-based multi-documents. The proposed method decomposes a sentence into the linear combination of sparse non-negative semantic features so that it can represent a sentence as the sum of a few semantic features that are comprehensible intuitively. It can improve the quality of document summaries because it can avoid extracting the sentences whose similarities with query are high but are meaningless by using the similarity between the query and the semantic features. Besides, it uses NMF clustering to remove noises so that it can avoid the biased inherent semantics of the documents to be reflected in summaries. Also it can ensure the coherence of summaries by using the rank score of sentences with respect to semantic features. The experimental results demonstrate that the proposed method has better performance than other methods using the thesaurus, the LSA, the K-means, and the NMF. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1109/NCM.2008.246 | NCM (2) |
Keywords | Field | DocType |
query-based multi-document summarization,pattern clustering,semantic feature,nmf clustering,document summary,meaningful sentence,comprehensible intuitively,better performance,nonnegative matrix factorization,matrix decomposition,query-based multidocument summarization,sparse nonnegative semantic feature,sparse non-negative semantic,non-negative matrix factorization,novel summarization method,text analysis,non-negative semantic feature,query processing,coherence,noise,k means,multi document summarization,feature extraction,data mining,non negative matrix factorization | Automatic summarization,Multi-document summarization,Pattern recognition,Computer science,Feature extraction,Artificial intelligence,Non-negative matrix factorization,Semantic feature,Cluster analysis,Sentence,Semantics | Conference |
Volume | ISBN | Citations |
2 | 978-0-7695-3322-3 | 1 |
PageRank | References | Authors |
0.35 | 9 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sun Park | 1 | 9 | 5.83 |
| ByungRae Cha | 2 | 51 | 14.59 |