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Reseach Article

An Approach for Concept-based Automatic Multi-Document Summarization using Machine Learning

by G. Padmapriya, K. Duraiswamy
International Journal of Applied Information Systems
Foundation of Computer Science (FCS), NY, USA
Volume 3 - Number 3
Year of Publication: 2012
Authors: G. Padmapriya, K. Duraiswamy

G. Padmapriya, K. Duraiswamy . An Approach for Concept-based Automatic Multi-Document Summarization using Machine Learning. International Journal of Applied Information Systems. 3, 3 ( July 2012), 49-53. DOI=http:/ijais12-450491

@article{ http:/ijais12-450491,
author = { G. Padmapriya, K. Duraiswamy },
title = { An Approach for Concept-based Automatic Multi-Document Summarization using Machine Learning },
journal = { International Journal of Applied Information Systems },
issue_date = { July 2012 },
volume = { 3 },
number = { 3 },
month = { July },
year = { 2012 },
issn = { 2249-0868 },
pages = { 49-53 },
numpages = {9},
url = { },
doi = { http:/ijais12-450491 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
%0 Journal Article
%1 2023-07-05T10:45:23.449662+05:30
%A G. Padmapriya
%A K. Duraiswamy
%T An Approach for Concept-based Automatic Multi-Document Summarization using Machine Learning
%J International Journal of Applied Information Systems
%@ 2249-0868
%V 3
%N 3
%P 49-53
%D 2012
%I Foundation of Computer Science (FCS), NY, USA

Text Summarization is compressing the source text into a shorter version preserving its information content and overall meaning. It is very complicated for human beings to manually summarize large documents of text. Text summarization plays an important role in the area of natural language processing and text mining. Many approaches use statistics and machine learning techniques to extract sentences from documents. This paper presents an new approach for concept-based automatic multi-document summarization using machine learning.

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Index Terms

Computer Science
Information Sciences


Multi-document Summarization Machine Learning