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

^*Corresponding author for this work

School of Computer Science and Technology

Beijing Institute of Technology

Research output: Contribution to journal › Conference article › peer-review

6 Citations (Scopus)

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.

Original language	English
Pages (from-to)	2944-2949
Number of pages	6
Journal	Procedia Engineering
Volume	29
DOIs	https://doi.org/10.1016/j.proeng.2012.01.419
Publication status	Published - 2012
Event	2012 International Workshop on Information and Electronics Engineering, IWIEE 2012 - Harbin, China Duration: 10 Mar 2012 → 11 Mar 2012

Keywords

Automatic text summarization
Latent Dirichlet Allocation
Similarity measure

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10.1016/j.proeng.2012.01.419

Cite this

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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",

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language = "English",

volume = "29",

pages = "2944--2949",

journal = "Procedia Engineering",

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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

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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

Abstract

Keywords

Access to Document

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