Open Access

ARTICLE

Latent Semantic Based Fuzzy Kernel Support Vector Machine for Automatic Content Summarization

T. Vetriselvi^1,*, J. Albert Mayan², K. V. Priyadharshini³, K. Sathyamoorthy⁴, S. Venkata Lakshmi⁵, P. Vishnu Raja⁶

1 Department of Computer Science and Engineering, K. Ramakrishnan College of Technology, Tiruchirappalli, 621112, India
2 Department of Computer Science and Engineering, Sathyabama Institute of Science and Technology, Chennai, 600119, India
3 Department of Information Technology, M. Kumarasamy College of Engineering, Karur, 639113, India
4 Department of Computer Science and Engineering, Panimalar Institute of Technology, Chennai, 600123, India
5 Department of Artificial Intelligence and Data Science, Sri Krishna College of Engineering and Technology, Coimbatore, 641008, India
6 Department of Computer Science and Engineering, Kongu Engineering College, Perundurai, 638060, India

* Corresponding Author: T. Vetriselvi. Email:

Intelligent Automation & Soft Computing 2022, 34(3), 1537-1551. https://doi.org/10.32604/iasc.2022.025235

Received 17 November 2021; Accepted 10 January 2022; Issue published 25 May 2022

Abstract

Recently, the bounteous amount of data/information has been available on the Internet which makes it very complicated to the customers to calculate the preferred data. Because the huge amount of data in a system is mandated to discover the most proper data from the corpus. Content summarization selects and extracts the related sentence depends upon the calculation of the score and rank of the corpus. Automatic content summarization technique translates from the higher corpus into smaller concise description. This chooses the very important level of the texts and implements the complete statistics summary. This paper proposes the novel technique that employs the latent semantic analysis (LSA) method where the LSA is derived from natural language processing. Also, it depends upon the particular threshold provided with the device. Statistical feature based model used to compact with inaccurate and ambiguity of the feature weights. Redundancy is removed with cosine similarity and it was presented an enhancement to the proposed method. Finally, fuzzy kernel support vector machine approach of machine learning technique is applied, so this novel model trains the classifier and predicts the statistics summary. This paper focuses to compare together with the another summarization dataset DUC (Document Understanding Conference) like ItemSum, Baseline, Summarizer, Recall Oriented Understudy for Gisting Evaluation (ROUGE) S and ROUGE L on DUC2007. The experiments and result section displays that our proposed model obtains an important performance improvement over the other classifier text summarizes.

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Keywords

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