Automatic Topic Identification for Idea Summarization in Idea Visualization Programs

Kobkrit VIRIYAYUDHAKORN; Susumu KUNIFUJI

doi:10.1587/transinf.E96.D.64

Automatic Topic Identification for Idea Summarization in Idea Visualization Programs

Kobkrit VIRIYAYUDHAKORN, Susumu KUNIFUJI

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Recent idea visualization programs still lack automatic idea summarization capabilities. This paper presents a knowledge-based method for automatically providing a short piece of English text about a topic to each idea group in idea charts. This automatic topic identification makes used Yet Another General Ontology (YAGO) and Wordnet as its knowledge bases. We propose a novel topic selection method and we compared its performance with three existing methods using two experimental datasets constructed using two idea visualization programs, i.e., the KJ Method (Kawakita Jiro Method) and mind-mapping programs. Our proposed topic identification method outperformed the baseline method in terms of both performance and consistency.

Publication: IEICE TRANSACTIONS on Information Vol.E96-D No.1 pp.64-72

Publication Date: 2013/01/01

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Online ISSN: 1745-1361

DOI: 10.1587/transinf.E96.D.64

Type of Manuscript: PAPER

Category: Artificial Intelligence, Data Mining

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Kobkrit VIRIYAYUDHAKORN, Susumu KUNIFUJI, "Automatic Topic Identification for Idea Summarization in Idea Visualization Programs" in IEICE TRANSACTIONS on Information, vol. E96-D, no. 1, pp. 64-72, January 2013, doi: 10.1587/transinf.E96.D.64.
Abstract: Recent idea visualization programs still lack automatic idea summarization capabilities. This paper presents a knowledge-based method for automatically providing a short piece of English text about a topic to each idea group in idea charts. This automatic topic identification makes used Yet Another General Ontology (YAGO) and Wordnet as its knowledge bases. We propose a novel topic selection method and we compared its performance with three existing methods using two experimental datasets constructed using two idea visualization programs, i.e., the KJ Method (Kawakita Jiro Method) and mind-mapping programs. Our proposed topic identification method outperformed the baseline method in terms of both performance and consistency.
URL: https://globals.ieice.org/en_transactions/information/10.1587/transinf.E96.D.64/_p

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@ARTICLE{e96-d_1_64,
author={Kobkrit VIRIYAYUDHAKORN, Susumu KUNIFUJI, },
journal={IEICE TRANSACTIONS on Information},
title={Automatic Topic Identification for Idea Summarization in Idea Visualization Programs},
year={2013},
volume={E96-D},
number={1},
pages={64-72},
abstract={Recent idea visualization programs still lack automatic idea summarization capabilities. This paper presents a knowledge-based method for automatically providing a short piece of English text about a topic to each idea group in idea charts. This automatic topic identification makes used Yet Another General Ontology (YAGO) and Wordnet as its knowledge bases. We propose a novel topic selection method and we compared its performance with three existing methods using two experimental datasets constructed using two idea visualization programs, i.e., the KJ Method (Kawakita Jiro Method) and mind-mapping programs. Our proposed topic identification method outperformed the baseline method in terms of both performance and consistency.},
keywords={},
doi={10.1587/transinf.E96.D.64},
ISSN={1745-1361},
month={January},}

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TY - JOUR
TI - Automatic Topic Identification for Idea Summarization in Idea Visualization Programs
T2 - IEICE TRANSACTIONS on Information
SP - 64
EP - 72
AU - Kobkrit VIRIYAYUDHAKORN
AU - Susumu KUNIFUJI
PY - 2013
DO - 10.1587/transinf.E96.D.64
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E96-D
IS - 1
JA - IEICE TRANSACTIONS on Information
Y1 - January 2013
AB - Recent idea visualization programs still lack automatic idea summarization capabilities. This paper presents a knowledge-based method for automatically providing a short piece of English text about a topic to each idea group in idea charts. This automatic topic identification makes used Yet Another General Ontology (YAGO) and Wordnet as its knowledge bases. We propose a novel topic selection method and we compared its performance with three existing methods using two experimental datasets constructed using two idea visualization programs, i.e., the KJ Method (Kawakita Jiro Method) and mind-mapping programs. Our proposed topic identification method outperformed the baseline method in terms of both performance and consistency.
ER -