Text Summarization Using Natural Language Processing
Year: 2025
Type: article
Abstract: Text summarization is a crucial task in natural language processing (NLP) that aims to condense large volumes of text into concise and informative summaries. This paper presents a comprehensive study of text summarization techniques using advanced NLP methods. The research focuses on extractive summarization, where key sentences or phrases are extracted from the original text to form a coherent summary. Various approaches such as graph-based algorithms, deep learning models, and hybrid methods combining linguistic features and neural networks are explored and evaluated. The paper also investigates the impact of domain-specific summarization techniques for specialized content areas. Experimental results on benchmark datasets demonstrate the effectiveness and scalability of the proposed methods compared to baseline summarization techniques. The findings contribute to advancing the state-of-the-art in text summarization, with implications for applications in information retrieval, document analysis, and automated content generation
Source: Journal of Electrical Systems
DOI: 10.52783/jes.8095
Author: Ashwini Mandale-Jadhav
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4407057939