A Comprehensive Review on Gujarati-Text Summarization Through Different Features

Authors

  • Riddhi Kevat  Research Scholar, Dept. of Computer Engineering, Sigma Institute of Engineering, Gujarat, India
  • Sheshang Degadwala  Associate Professor & Head of Department, Dept. of Computer Engineering, Sigma University, Gujarat, India

DOI:

https://doi.org/10.32628/CSEIT2361051

Keywords:

Gujarati-Text Summarization, Linguistic Features, Semantic Analysis, Syntactic Elements, Natural Language Processing, Machine Learning.

Abstract

This comprehensive review delves into the intricacies of Gujarati-text summarization, exploring diverse features employed in the process. With a focus on the nuances of the Gujarati language, the paper investigates various techniques and methodologies applied to extract essential information from textual content. The review systematically examines the effectiveness of distinct features such as linguistic, semantic, and syntactic elements in the context of Gujarati summarization. Additionally, the study provides insights into the challenges specific to Gujarati-language summarization and discusses advancements in natural language processing and machine learning that contribute to the refinement of summarization models. This thorough examination serves as a valuable resource for researchers, practitioners, and enthusiasts seeking a deeper understanding of the complexities and advancements in Gujarati-text summarization.

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IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

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Published

2023-10-30

Issue

Volume 9, Issue 10, September-October-2023

Section

Research Articles

License

Copyright (c) IJSRCSEIT

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This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Riddhi Kevat, Sheshang Degadwala, " A Comprehensive Review on Gujarati-Text Summarization Through Different Features " International Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 9, Issue 10, pp.301-306, September-October-2023. Available at doi : https://doi.org/10.32628/CSEIT2361051