The similarity measure based on LDA for automatic summarization

Tiedan Zhu; Kan Li

doi:10.1016/j.proeng.2012.01.419

The similarity measure based on LDA for automatic summarization

Tiedan Zhu^*, Kan Li

^*此作品的通讯作者

计算机学院

Beijing Institute of Technology

科研成果: 期刊稿件 › 会议文章 › 同行评审

6 引用（Scopus）

摘要

This paper proposes a novel similarity measure for automatic text summarization. The topic space model is built through the Latent Dirichlet Allocation. The word, sentence, document and corpus are represented as vectors in the same topic space. LMMR and LSD algorithm are introduced to create the summary. An experiment is illustrated on DUC data and the results prove the proposed measure and algorithm effective and well performed.

源语言	英语
页（从-至）	2944-2949
页数	6
期刊	Procedia Engineering
卷	29
DOI	https://doi.org/10.1016/j.proeng.2012.01.419
出版状态	已出版 - 2012
活动	2012 International Workshop on Information and Electronics Engineering, IWIEE 2012 - Harbin, 中国期限: 10 3月 2012 → 11 3月 2012

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10.1016/j.proeng.2012.01.419

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引用此

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title = "The similarity measure based on LDA for automatic summarization",

abstract = "This paper proposes a novel similarity measure for automatic text summarization. The topic space model is built through the Latent Dirichlet Allocation. The word, sentence, document and corpus are represented as vectors in the same topic space. LMMR and LSD algorithm are introduced to create the summary. An experiment is illustrated on DUC data and the results prove the proposed measure and algorithm effective and well performed.",

keywords = "Automatic text summarization, Latent Dirichlet Allocation, Similarity measure",

author = "Tiedan Zhu and Kan Li",

year = "2012",

doi = "10.1016/j.proeng.2012.01.419",

language = "English",

volume = "29",

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journal = "Procedia Engineering",

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note = "2012 International Workshop on Information and Electronics Engineering, IWIEE 2012 ; Conference date: 10-03-2012 Through 11-03-2012",

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T1 - The similarity measure based on LDA for automatic summarization

AU - Zhu, Tiedan

AU - Li, Kan

PY - 2012

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N2 - This paper proposes a novel similarity measure for automatic text summarization. The topic space model is built through the Latent Dirichlet Allocation. The word, sentence, document and corpus are represented as vectors in the same topic space. LMMR and LSD algorithm are introduced to create the summary. An experiment is illustrated on DUC data and the results prove the proposed measure and algorithm effective and well performed.

AB - This paper proposes a novel similarity measure for automatic text summarization. The topic space model is built through the Latent Dirichlet Allocation. The word, sentence, document and corpus are represented as vectors in the same topic space. LMMR and LSD algorithm are introduced to create the summary. An experiment is illustrated on DUC data and the results prove the proposed measure and algorithm effective and well performed.

KW - Automatic text summarization

KW - Latent Dirichlet Allocation

KW - Similarity measure

UR - http://www.scopus.com/inward/record.url?scp=84858419143&partnerID=8YFLogxK

U2 - 10.1016/j.proeng.2012.01.419

DO - 10.1016/j.proeng.2012.01.419

M3 - Conference article

AN - SCOPUS:84858419143

SN - 1877-7058

VL - 29

SP - 2944

EP - 2949

JO - Procedia Engineering

JF - Procedia Engineering

T2 - 2012 International Workshop on Information and Electronics Engineering, IWIEE 2012

Y2 - 10 March 2012 through 11 March 2012

ER -

The similarity measure based on LDA for automatic summarization

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