A Comprehensive Review on Gujarati-Text Summarization Through Different Features-Reference-Cited by-同舟云学术

A Comprehensive Review on Gujarati-Text Summarization Through Different Features

Published:2023-11-10 Issue: Volume: Page:301-306
ISSN:2456-3307
Container-title:International Journal of Scientific Research in Computer Science, Engineering and Information Technology
language:en
Short-container-title:IJSRCSEIT

Author:

Kevat Riddhi¹,Degadwala Sheshang²

Affiliation:

1. Research Scholar, Dept. of Computer Engineering, Sigma Institute of Engineering, Gujarat, India

2. Associate Professor & Head of Department, Dept. of Computer Engineering, Sigma University, Gujarat, India

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.

Publisher

Technoscience Academy

Subject

General Earth and Planetary Sciences,General Environmental Science

Reference15 articles.

1. J. Bhayo, S. A. Shah, S. Hameed, A. Ahmed, J. Nasir, and D. Draheim, “Towards a machine learning-based framework for DDOS attack detection in software-defined IoT (SD-IoT) networks,” Engineering Applications of Artificial Intelligence, vol. 123, no. July 2022, p. 106432, 2023, doi: 10.1016/j.engappai.2023.106432.

2. F. S. de Lima Filho, F. A. F. Silveira, A. de Medeiros Brito Junior, G. Vargas-Solar, and L. F. Silveira, “Smart detection: An online approach for DoS/DDoS attack detection using machine learning,” Secur. Commun. Netw., vol. 2019, pp. 1–15, 2019.

3. S. U. Jan, S. Ahmed, V. Shakhov, and I. Koo, “Toward a lightweight intrusion detection system for the internet of things,” IEEE Access, vol. 7, pp. 42450–42471, undefined 2019.

4. Y. Otoum and A. Nayak, “AS-IDS: Anomaly and signature-based IDS for the internet of things,” J. Netw. Syst. Manag., vol. 29, no. 3, 2021.

5. R. Zagrouba and R. AlHajri, “Machine Learning based attacks detection and countermeasures in IoT,” International j. commun. netw. inf. secur., vol. 13, no. 2, 2021.