CFP last date
20 May 2024
Reseach Article

Review of Text Reduction Algorithms and Text Reduction using Sentence Vectorization

by Sneh Garg, Sunil Chhillar
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 107 - Number 12
Year of Publication: 2014
Authors: Sneh Garg, Sunil Chhillar
10.5120/18806-0380

Sneh Garg, Sunil Chhillar . Review of Text Reduction Algorithms and Text Reduction using Sentence Vectorization. International Journal of Computer Applications. 107, 12 ( December 2014), 39-42. DOI=10.5120/18806-0380

@article{ 10.5120/18806-0380,
author = { Sneh Garg, Sunil Chhillar },
title = { Review of Text Reduction Algorithms and Text Reduction using Sentence Vectorization },
journal = { International Journal of Computer Applications },
issue_date = { December 2014 },
volume = { 107 },
number = { 12 },
month = { December },
year = { 2014 },
issn = { 0975-8887 },
pages = { 39-42 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume107/number12/18806-0380/ },
doi = { 10.5120/18806-0380 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:40:54.639458+05:30
%A Sneh Garg
%A Sunil Chhillar
%T Review of Text Reduction Algorithms and Text Reduction using Sentence Vectorization
%J International Journal of Computer Applications
%@ 0975-8887
%V 107
%N 12
%P 39-42
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The reduced text of a document is the collection of sentences that contains the important sentences containing keywords of the document. The authentic keywords extraction is the primary target for any text reduction algorithm. The presented survey shows the primary algorithm used for document summarization based on keywords. Also, the work presents a novel approach for keywords identification and in turn text reduction based on words histogram, the no. of sentences containing the words and knowledge corpus. The text summary is extracted using the sentence vectorization process. The sentence vectorization gives the sentences that have at least one of the key words in the sentence from the entire document. The algorithm works fine for the textual matter in the document in MS Notepad format. Factual information that is normally covered under double inverted comas is also given due attention in text summary.

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

Text Reduction Text summary Sentence Vectorization Word Histogram Reduction algorithm Synonyms