@Article{iasc.2022.025235, AUTHOR = {T. Vetriselvi, J. Albert Mayan, K. V. Priyadharshini, K. Sathyamoorthy, S. Venkata Lakshmi, P. Vishnu Raja}, TITLE = {Latent Semantic Based Fuzzy Kernel Support Vector Machine for Automatic Content Summarization}, JOURNAL = {Intelligent Automation \& Soft Computing}, VOLUME = {34}, YEAR = {2022}, NUMBER = {3}, PAGES = {1537--1551}, URL = {http://www.techscience.com/iasc/v34n3/47919}, ISSN = {2326-005X}, 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.}, DOI = {10.32604/iasc.2022.025235} }