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Context Score based Term Weighting Model for Text Summarization

by Pratik Kamble, S. C. Dharamadhikari
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 98 - Number 12
Year of Publication: 2014
Authors: Pratik Kamble, S. C. Dharamadhikari
10.5120/17238-7572

Pratik Kamble, S. C. Dharamadhikari . Context Score based Term Weighting Model for Text Summarization. International Journal of Computer Applications. 98, 12 ( July 2014), 41-46. DOI=10.5120/17238-7572

@article{ 10.5120/17238-7572,
author = { Pratik Kamble, S. C. Dharamadhikari },
title = { Context Score based Term Weighting Model for Text Summarization },
journal = { International Journal of Computer Applications },
issue_date = { July 2014 },
volume = { 98 },
number = { 12 },
month = { July },
year = { 2014 },
issn = { 0975-8887 },
pages = { 41-46 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume98/number12/17238-7572/ },
doi = { 10.5120/17238-7572 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:26:03.539326+05:30
%A Pratik Kamble
%A S. C. Dharamadhikari
%T Context Score based Term Weighting Model for Text Summarization
%J International Journal of Computer Applications
%@ 0975-8887
%V 98
%N 12
%P 41-46
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Everybody is looking for relevant information briefly, which will cover information with small content. Summarization is the best for this. Current text summarization techniques do not consider the context i. e. background situation in that document. In this paper we are going to present the SentenceRank algorithm which will calculate the weight of the sentence based on the context score. We are going to make effective use of E-VSM : Enhance - Vector Space Model for bigram frequency count in whole corpus, where for each bigram we are going calculate the context score based on Bernoulli's model of randomness [1] [2]. Calculated bigrams context score is used in sentenceRank algorithm to calculate the context sensitive indexing weight of each sentence in a document. To reduce the redundancy in the sentences of summary, Cosine similarity measure is used to remove redundant sentence.

References
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Index Terms

Computer Science
Information Sciences

Keywords

Context Score E-VSM SentenceRank

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