Design & Development of a TLBO – based Metaheuristic Optimization Framework for Extractive Text Summarization

Extractive text summarization based on metaheuristics turns out to be a potent way to deal with the constantly increasing amount of digital data. By leveraging algorithms such as Firefly, Frog Leaping, and Teaching-Learning-Based optimization, these approaches effectively balance relevance, coherence, and coverage while minimizing redundancy. Compared to conventional methods, they demonstrate superior performance on benchmark datasets and hold strong potential for multilingual, domain-specific, and real-time applications. Hence, metaheuristic-driven summarization stands as a promising direction for advancing efficient and intelligent information retrieval in NLP.

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