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A Comprehensive Analysis on Diverse Deep Learning Methods on Text Summarisation and Classification Techniques
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Author(s): B. Raju (Bharath Institute of Higher Education and Research, India), M. Sriram (Bharath Institute of Higher Education and Research, India)and V. Ganesan (Bharath Institute of Higher Education and Research, India)
Copyright: 2026
Pages: 16
Source title:
Machine Learning, Predictive Analytics, and Optimization in Complex Systems
Source Author(s)/Editor(s): Ferdin Joe John Joseph (Thai-Nichi Institute of Technology, Thailand), Karthikeyan Chinnusamy (Veritas, USA), Joseph Jeganathan (University of Bahrain, Bahrain), Ahmed J. Obaid (University of Kufa, Iraq)and S. Suman Rajest (Dhaanish Ahmed College of Engineering, India)
DOI: 10.4018/979-8-3373-5203-9.ch012
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Abstract
Social media platforms employ text summarization and classification techniques to generate summaries for posts grouped based on topics' content. It is also used in the search engine to answer the user query rather than just providing a link. The text classification and summarisation techniques produce a summary containing important information from the original document and all the relevant sentences. Two major approaches, extractive and abstractive, are viewed from the summary results. Many difficulties exist, such as missing out on certain sentences, affecting the summary meaning, and not meeting the required standards. The proposed machine learning-based feature selection method extracts the source information and produces the end user's proper information. Statistic techniques combined with machine learning algorithms and fuzzy logic will quickly determine the keywords and similarity of the word and scale it to different languages.
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