JOURNAL ARTICLE

Subtopic-based multi-document summarization

Abstract

This paper proposes a novel approach for multi-document summarization based on subtopic segmentation. It firstly detects the subtopics in a topic, and then finds the central sentence for each subtopic. The sentences are scored based on their importance in the document and in the subtopic. Two anti-redundancy strategies are used to extract sentences to form summarization. Since our approach is intrinsically incremental, it is effective when new documents are added to the document set. Experimental results indicate that the proposed approach is effective and efficient.

Keywords:
Automatic summarization Computer science Redundancy (engineering) Sentence Multi-document summarization Set (abstract data type) Segmentation Natural language processing Information retrieval Artificial intelligence

Metrics

4
Cited By
0.76
FWCI (Field Weighted Citation Impact)
14
Refs
0.84
Citation Normalized Percentile
Is in top 1%
Is in top 10%

Citation History

Topics

Topic Modeling
Physical Sciences →  Computer Science →  Artificial Intelligence
Natural Language Processing Techniques
Physical Sciences →  Computer Science →  Artificial Intelligence
Advanced Text Analysis Techniques
Physical Sciences →  Computer Science →  Artificial Intelligence
© 2026 ScienceGate Book Chapters — All rights reserved.