Abstract :

With The Exponential Growth Of Digital Text Data, Efficiently Extracting Meaningful Information Has Become A Significant Challenge. Text Summarization Aims To Generate Concise And Informative Summaries While Preserving The Essential Content Of Documents. The Proposed System, Text Summarization Using Firefly Algorithm (FA), Leverages The Nature-inspired Optimization Technique Of Fireflies To Select The Most Relevant Sentences From A Document And Construct An Accurate Summary. The Firefly Algorithm Is Used To Optimize Sentence Selection Based On Criteria Such As Sentence Importance, Similarity, Relevance, And Redundancy. Each Firefly Represents A Potential Solution (a Subset Of Sentences), And Their Movements Toward Brighter (more Optimal) Fireflies Ensure That The Algorithm Iteratively Identifies The Best Combination Of Sentences For The Summary. By Applying This Bio-inspired Approach, The System Can Efficiently Handle Large Text Corpora And Generate High-quality Summaries That Retain The Key Information While Eliminating Redundant Or Irrelevant Content. Experimental Results Demonstrate That The Proposed FA-based Summarization Technique Provides Better Coherence, Coverage, And Relevance Compared To Conventional Methods, Making It A Robust And Scalable Solution For Applications Such As News Aggregation, Document Analysis, And Information Retrieval. Keywords: Text Summarization, Firefly Algorithm, Optimization, Sentence Selection, Information Extraction, Bio-inspired Computing, Document Summarization.

Published:

28-10-2025

Issue:

Vol. 25 No. 10 (2025)

Page Nos:

217-221

Section:

Articles

License:

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

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