Sentence Extraction by Spreading Activation through Sentence Similarity

Naoaki OKAZAKI; Yutaka MATSUO; Naohiro MATSUMURA; Mitsuru ISHIZUKA

Sentence Extraction by Spreading Activation through Sentence Similarity

Naoaki OKAZAKI, Yutaka MATSUO, Naohiro MATSUMURA, Mitsuru ISHIZUKA

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Although there has been a great deal of research on automatic summarization, most methods rely on statistical methods, disregarding relationships between extracted textual segments. We propose a novel method to extract a set of comprehensible sentences which centers on several key points to ensure sentence connectivity. It features a similarity network from documents with a lexical dictionary, and spreading activation to rank sentences. We show evaluation results of a multi-document summarization system based on the method participating in a competition of summarization, TSC (Text Summarization Challenge) task, organized by the third NTCIR project.

Publication: IEICE TRANSACTIONS on Information Vol.E86-D No.9 pp.1686-1694

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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Naoaki OKAZAKI, Yutaka MATSUO, Naohiro MATSUMURA, Mitsuru ISHIZUKA, "Sentence Extraction by Spreading Activation through Sentence Similarity" in IEICE TRANSACTIONS on Information, vol. E86-D, no. 9, pp. 1686-1694, September 2003, doi: .
Abstract: Although there has been a great deal of research on automatic summarization, most methods rely on statistical methods, disregarding relationships between extracted textual segments. We propose a novel method to extract a set of comprehensible sentences which centers on several key points to ensure sentence connectivity. It features a similarity network from documents with a lexical dictionary, and spreading activation to rank sentences. We show evaluation results of a multi-document summarization system based on the method participating in a competition of summarization, TSC (Text Summarization Challenge) task, organized by the third NTCIR project.
URL: https://globals.ieice.org/en_transactions/information/10.1587/e86-d_9_1686/_p

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@ARTICLE{e86-d_9_1686,
author={Naoaki OKAZAKI, Yutaka MATSUO, Naohiro MATSUMURA, Mitsuru ISHIZUKA, },
journal={IEICE TRANSACTIONS on Information},
title={Sentence Extraction by Spreading Activation through Sentence Similarity},
year={2003},
volume={E86-D},
number={9},
pages={1686-1694},
abstract={Although there has been a great deal of research on automatic summarization, most methods rely on statistical methods, disregarding relationships between extracted textual segments. We propose a novel method to extract a set of comprehensible sentences which centers on several key points to ensure sentence connectivity. It features a similarity network from documents with a lexical dictionary, and spreading activation to rank sentences. We show evaluation results of a multi-document summarization system based on the method participating in a competition of summarization, TSC (Text Summarization Challenge) task, organized by the third NTCIR project.},
keywords={},
doi={},
ISSN={},
month={September},}

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TY - JOUR
TI - Sentence Extraction by Spreading Activation through Sentence Similarity
T2 - IEICE TRANSACTIONS on Information
SP - 1686
EP - 1694
AU - Naoaki OKAZAKI
AU - Yutaka MATSUO
AU - Naohiro MATSUMURA
AU - Mitsuru ISHIZUKA
PY - 2003
DO -
JO - IEICE TRANSACTIONS on Information
SN -
VL - E86-D
IS - 9
JA - IEICE TRANSACTIONS on Information
Y1 - September 2003
AB - Although there has been a great deal of research on automatic summarization, most methods rely on statistical methods, disregarding relationships between extracted textual segments. We propose a novel method to extract a set of comprehensible sentences which centers on several key points to ensure sentence connectivity. It features a similarity network from documents with a lexical dictionary, and spreading activation to rank sentences. We show evaluation results of a multi-document summarization system based on the method participating in a competition of summarization, TSC (Text Summarization Challenge) task, organized by the third NTCIR project.
ER -