We apologize for a recent technical issue with our email system, which temporarily affected account activations. Accounts have now been activated. Authors may proceed with paper submissions. PhDFocusTM
CFP last date
20 December 2024
Call for Paper
January Edition
IJCA solicits high quality original research papers for the upcoming January edition of the journal. The last date of research paper submission is 20 December 2024

Submit your paper
Know more
The week's pick
Responsive image
Improved Shuffled Frog Leaping Algorithm with Self-Adaptive Shuffling for Fuzzy Logic PD+G Controller Optimization in Robotic Manipulators

Duc Hoang Nguyen
Random Articles
Reseach Article

Document Summarization and Evaluation using Knowledge based Super Set Features

by Sneh Garg, Sunil Chhillar
journal cover thumbnail

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.

References
  1. Durga Bhavani Dasari, Dr. Venu gopala Rao. K. , "Single Document Text Summarization by Knowledge-Corpus", 978-1-4799-1626-9/ 2013 IEEE.
  2. Li Chengcheng," Automatic Text Summarization Based On Rhetorical Structure Theory", 978-1-4244-7237-62010 IEEE.
  3. Te-Min Chang, Wen-Feng Hsiao," A hybrid approach to automatic text summarization", 978-1-4244-2358-3/2008 IEEE.
  4. Suneetha Manne, S. Sameen Fatima," A Feature Terms based Method for Improving Text Summarization with Supervised POS Tagging", International Journal of Computer Applications (0975 – 8887) Volume 47– No. 23, June 2012.
  5. Nowshath K. Batcha, Normaziah A. Aziz," CRF Based Feature Extraction Applied for Supervised AutomaticText Summarization", Procedia Technology 11 (2013) 426 – 436.
  6. K. Nandhini, S. R. Balasundaram," Improving readability through extractive summarization for learners with reading difficulties", Egyptian Informatics Journal (2013) 14, 195–204.
  7. Alexander Yates, Oren Etzioni," Unsupervised Methods for Determining Object and Relation Synonyms on the Web", Journal of Artificial Intelligence Research 34 (2009) 255-296.
  8. Vipul Dalal, Dr. Latesh Malik,"A Survey of Extractive and Abstractive Automatic Text summarization Techniques", 978-1-4799-2560-5/2013 IEEE DOI 10. 1109/ICETET. 2013. 31.
  9. Egitim Fakültesi, Mehmet Akif Ersoy," Quality of written summary texts: An analysis in the context of gender and school variables", 1877-0428 © 2010 Published by Elsevier Ltd.
  10. Donia Scott, Catalina Hallett, Rachel Fettiplace," Data-to-text summarisation of patient records: Using computer-generated summaries to access patient histories", D. Scott 156 et al. / Patient Education and Counseling 92 (2013) 153–159
  11. Kushal Bafna, Durga Toshniwal," Feature Based Summarization of Customers' Reviews of Online Products", 2013 The Authors. Published by Elsevier B. V.
  12. Tiedan Zhu, Kan Li," The Similarity Measure Based on LDA for Automatic Summarization", 2011 Published by Elsevier Ltd
Index Terms

Computer Science
Information Sciences

Keywords

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

Powered by PhDFocusTM