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Document Summarization and Evaluation using Knowledge based Super Set Features

by Sneh Garg, Sunil Chhillar
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 113 - Number 2
Year of Publication: 2015
Authors: Sneh Garg, Sunil Chhillar
10.5120/19802-1585

Sneh Garg, Sunil Chhillar . Document Summarization and Evaluation using Knowledge based Super Set Features. International Journal of Computer Applications. 113, 2 ( March 2015), 41-44. DOI=10.5120/19802-1585

@article{ 10.5120/19802-1585,
author = { Sneh Garg, Sunil Chhillar },
title = { Document Summarization and Evaluation using Knowledge based Super Set Features },
journal = { International Journal of Computer Applications },
issue_date = { March 2015 },
volume = { 113 },
number = { 2 },
month = { March },
year = { 2015 },
issn = { 0975-8887 },
pages = { 41-44 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume113/number2/19802-1585/ },
doi = { 10.5120/19802-1585 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:49:57.850145+05:30
%A Sneh Garg
%A Sunil Chhillar
%T Document Summarization and Evaluation using Knowledge based Super Set Features
%J International Journal of Computer Applications
%@ 0975-8887
%V 113
%N 2
%P 41-44
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Document summarization is an important step while clustering the large no. of digital documents data base. Documents are clustered in accordance with their contents using the document text summary. The document summarization involves the knowledge corpus scheme comprising of corpus coverage, sentence coverage and term coverage weight. Further, three new weights are introduced as super sentence coverage weight, super corpus coverage weight and super term coverage weight. Super coverage weight is based on synonyms of the key words. The quality of document summary improves and diversified when synonyms of key words are also given due weightage in the process of text processing. The evaluation for the document summary quality is based on inner content metrics precision, recall, F-measure method.

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Index Terms

Computer Science
Information Sciences

Keywords

Super Sentence Coverage Weight Super Corpus Coverage Weight Super Term Coverage Weight Document Summarization Knowledge Corpus Synonyms

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