JOURNAL ARTICLE

MULTI-DOCUMENT TEXT SUMMARIZATION USING CLUSTERING TECHNIQUES AND LEXICAL CHAINING

Abstract

This paper investigates the use of clustering and lexical chains to produce coherent summaries of multiple documents in text format to generate an indicative, less redundant summary. The summary is designed as per user’s requirement of conciseness i.e., the documents are summarized according to the percentage input by the user. For achieving the above, various clustering techniques are used. Clustering is done at two levels, one at single document level and then at multi-document level. The clustered sentences are scored based on five different methods and lexically linked to produce the final summary in a text document.

Keywords:
Automatic summarization Chaining Computer science Quality (philosophy) Cluster analysis Information retrieval Data science World Wide Web Psychology Artificial intelligence

Metrics

6
Cited By
0.00
FWCI (Field Weighted Citation Impact)
9
Refs
0.16
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

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