eprintid: 7522
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/00/75/22
datestamp: 2023-11-09 16:19:20
lastmod: 2023-11-09 16:19:20
status_changed: 2023-11-09 16:09:38
type: article
metadata_visibility: show
creators_name: Foong, O.-M.
creators_name: Yong, S.-P.
title: Swarm LSA-PSO clustering model in text summarization
ispublished: pub
note: cited By 4
abstract: The information overload problem has posed great challenge to internet users to retrieve relevant information accurately for the past decades. It is a tedious task for machine to intuitively mimic human linguists to summarize documents into meaningful text in abstractive manner. Quite often, the summarized text lacks cohesion and becomes difficult to comprehend. The objective of this paper is to investigate the proposed Swarm LSA-PSO model performs better than alternative methods. In this study, terms matrix was constructed from co-occurrence of terms using Bag-of-Words (BOW). The huge dimensions of terms were reduced using Singular Value Decomposition followed by K-Means PSO clustering for acquiring optimal number of concepts clusters. These key concepts were used to identify the main gist in documents for text summarization. The input text documents were downloaded from Document Understanding Conference (DUC) 2002 dataset. The preliminary results show that the swarm LSA-PSO model shows promising results in context based text summarization using BOW clustering approach.
date: 2016
publisher: International Center for Scientific Research and Studies
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85010379062&partnerID=40&md5=2056144ceacce2cb313b32090b6e0706
full_text_status: none
publication: International Journal of Advances in Soft Computing and its Applications
volume: 8
number: 3
pagerange: 88-99
refereed: TRUE
issn: 20748523
citation:   Foong, O.-M. and Yong, S.-P.  (2016) Swarm LSA-PSO clustering model in text summarization.  International Journal of Advances in Soft Computing and its Applications, 8 (3).  pp. 88-99.  ISSN 20748523