Automatic Text Summarization Using Latent Drichlet Allocation (LDA) for Document Clustering

International Journal of Advances in Intelligent Informatics

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Field Value
 
Title Automatic Text Summarization Using Latent Drichlet Allocation (LDA) for Document Clustering
 
Creator Hidayat, Erwin Yudi
Firdausillah, Fahri
Hastuti, Khafiizh
Dewi, Ika Novita
Azhari, Azhari
 
Subject LDA, text summarization, clustering, k-means
 
Description In this paper, we present Latent Drichlet Allocation in automatic text summarization to improve accuracy in document clustering. The experiments involving 398 data set from public blog article obtained by using python scrapy crawler and scraper. Several steps of clustering in this research are preprocessing, automatic document compression using feature method, automatic document compression using LDA, word weighting and clustering algorithm The results show that automatic document summarization with LDA reaches 72% in LDA 40%, compared to traditional k-means method which only reaches 66%.
 
Publisher Universitas Ahmad Dahlan
 
Contributor
 
Date 2015-12-01
 
Type info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion

 
Format application/pdf
 
Identifier http://ijain.org/index.php/IJAIN/article/view/43
10.26555/ijain.v1i3.43
 
Source International Journal of Advances in Intelligent Informatics; Vol 1, No 3 (2015): November 2015; 132-139
2548-3161
2442-6571
 
Language eng
 
Relation http://ijain.org/index.php/IJAIN/article/view/43/ijain_v1i3_p132_139
http://ijain.org/index.php/IJAIN/article/downloadSuppFile/43/21
 
Rights Copyright (c) 2015 International Journal of Advances in Intelligent Informatics
http://creativecommons.org/licenses/by-sa/4.0
 

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