IEICE TRANSACTIONS on Information
Topic Keyword Identification for Text Summarization Using Lexical Clustering
Summary :
Automatic text summarization has the goal of reducing the size of a document while preserving its content. Generally, producing a summary as extracts is achieved by including only sentences which are the most topic-related. DOCUSUM is our summarization system based on a new topic keyword identification method. The process of DOCUSUM is as follows. First, DOCUSUM converts the content words of a document into elements of a context vector space. It then constructs lexical clusters from the context vector space and identifies core clusters. Next, it selects topic keywords from the core clusters. Finally, it generates a summary of the document using the topic keywords. In the experiments on various compression ratios (the compression of 30%, the compression of 10%, and the extraction of the fixed number of sentences: 4 or 8 sentences), DOCUSUM showed better performance than other methods.
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- IEICE TRANSACTIONS on Information Vol.E86-D No.9 pp.1695-1701
- Publication Date
- 2003/09/01
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- Type of Manuscript
- Special Section PAPER (Special Issue on Text Processing for Information Access)
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Youngjoong KO, Kono KIM, Jungyun SEO, "Topic Keyword Identification for Text Summarization Using Lexical Clustering" in IEICE TRANSACTIONS on Information,
vol. E86-D, no. 9, pp. 1695-1701, September 2003, doi: .
Abstract: Automatic text summarization has the goal of reducing the size of a document while preserving its content. Generally, producing a summary as extracts is achieved by including only sentences which are the most topic-related. DOCUSUM is our summarization system based on a new topic keyword identification method. The process of DOCUSUM is as follows. First, DOCUSUM converts the content words of a document into elements of a context vector space. It then constructs lexical clusters from the context vector space and identifies core clusters. Next, it selects topic keywords from the core clusters. Finally, it generates a summary of the document using the topic keywords. In the experiments on various compression ratios (the compression of 30%, the compression of 10%, and the extraction of the fixed number of sentences: 4 or 8 sentences), DOCUSUM showed better performance than other methods.
URL: https://global.ieice.org/en_transactions/information/10.1587/e86-d_9_1695/_p
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@ARTICLE{e86-d_9_1695,
author={Youngjoong KO, Kono KIM, Jungyun SEO, },
journal={IEICE TRANSACTIONS on Information},
title={Topic Keyword Identification for Text Summarization Using Lexical Clustering},
year={2003},
volume={E86-D},
number={9},
pages={1695-1701},
abstract={Automatic text summarization has the goal of reducing the size of a document while preserving its content. Generally, producing a summary as extracts is achieved by including only sentences which are the most topic-related. DOCUSUM is our summarization system based on a new topic keyword identification method. The process of DOCUSUM is as follows. First, DOCUSUM converts the content words of a document into elements of a context vector space. It then constructs lexical clusters from the context vector space and identifies core clusters. Next, it selects topic keywords from the core clusters. Finally, it generates a summary of the document using the topic keywords. In the experiments on various compression ratios (the compression of 30%, the compression of 10%, and the extraction of the fixed number of sentences: 4 or 8 sentences), DOCUSUM showed better performance than other methods.},
keywords={},
doi={},
ISSN={},
month={September},}
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TY - JOUR
TI - Topic Keyword Identification for Text Summarization Using Lexical Clustering
T2 - IEICE TRANSACTIONS on Information
SP - 1695
EP - 1701
AU - Youngjoong KO
AU - Kono KIM
AU - Jungyun SEO
PY - 2003
DO -
JO - IEICE TRANSACTIONS on Information
SN -
VL - E86-D
IS - 9
JA - IEICE TRANSACTIONS on Information
Y1 - September 2003
AB - Automatic text summarization has the goal of reducing the size of a document while preserving its content. Generally, producing a summary as extracts is achieved by including only sentences which are the most topic-related. DOCUSUM is our summarization system based on a new topic keyword identification method. The process of DOCUSUM is as follows. First, DOCUSUM converts the content words of a document into elements of a context vector space. It then constructs lexical clusters from the context vector space and identifies core clusters. Next, it selects topic keywords from the core clusters. Finally, it generates a summary of the document using the topic keywords. In the experiments on various compression ratios (the compression of 30%, the compression of 10%, and the extraction of the fixed number of sentences: 4 or 8 sentences), DOCUSUM showed better performance than other methods.
ER -
English
