JOURNAL ARTICLE

Multi-document summarization using sentence clustering

Abstract

This paper presents an approach to query focused multi document summarization by combining single document summary using sentence clustering. Both syntactic and semantic similarity between sentences is used for clustering. Single document summary is generated using document feature, sentence reference index feature, location feature and concept similarity feature. Sentences from single document summaries are clustered and top most sentences from each cluster are used for creating multi-document summary. We observed an average F-measure of 0.33774 on DUC 2002 multi-document dataset, which is comparable to three best performing systems reported on the same dataset.

Keywords:
Automatic summarization Computer science Document clustering Sentence Cluster analysis Feature (linguistics) Artificial intelligence Information retrieval Natural language processing Multi-document summarization Similarity (geometry) Index (typography) World Wide Web Linguistics

Metrics

48
Cited By
1.89
FWCI (Field Weighted Citation Impact)
12
Refs
0.88
Citation Normalized Percentile
Is in top 1%
Is in top 10%

Citation History

Topics

Natural Language Processing Techniques
Physical Sciences →  Computer Science →  Artificial Intelligence
Topic Modeling
Physical Sciences →  Computer Science →  Artificial Intelligence
Semantic Web and Ontologies
Physical Sciences →  Computer Science →  Artificial Intelligence
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