Automatic text summarization based on sentences clustering and extraction

Technology of automatic text summarization plays an important role in information retrieval and text classification, and may provide a solution to the information overload problem. Text summarization is a process of reducing the size of a text while preserving its information content. This paper pro...

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Bibliographic Details
Published in:2009 2nd IEEE International Conference on Computer Science and Information Technology pp. 167 - 170
Main Authors: Zhang Pei-ying, Li Cun-he
Format: Conference Proceeding
Language:English
Published: IEEE 01.08.2009
Subjects:
Clustering algorithms
Data mining
Educational institutions
Information retrieval
Natural language processing
Petroleum
sentence extractive technique
sentences clustering
similarity measure
Text categorization
text summarization
Volume measurement
Web sites
ISBN:1424445191, 9781424445196
Online Access:Get full text
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Summary:Technology of automatic text summarization plays an important role in information retrieval and text classification, and may provide a solution to the information overload problem. Text summarization is a process of reducing the size of a text while preserving its information content. This paper proposes a sentences clustering based summarization approach. The proposed approach consists of three steps: first clusters the sentences based on the semantic distance among sentences in the document, and then on each cluster calculates the accumulative sentence similarity based on the multi-features combination method, at last chooses the topic sentences by some extraction rules. The purpose of present paper is to show that summarization result is not only depends the sentence features, but also depends on the sentence similarity measure. The experimental result on the DUC 2003 dataset show that our proposed approach can improve the performance compared to other summarization methods.
ISBN:1424445191
9781424445196
DOI:10.1109/ICCSIT.2009.5234971