Text Summarisation Using Swarm-Based and Genetic-Based Methods in Natural Language Processing: A Comparative Review
DOI:
https://doi.org/10.54536/jnll.v3i1.3079Keywords:
Genetic-Based Methods, Natural Language Processing, Particle Swarm Optimization, Swarm-Based Methods, Text SummarizationAbstract
The digital age has resulted in unparalleled data availability, posing a serious problem of information overload. As a result, automatic text summarizing has evolved as an important technique for compressing large volumes of data into concise, consumable summaries such as Swarm-Based and Genetic-Based approaches. This comparative review investigates the use of those two approaches in text summarization within Natural Language Processing (NLP). Using an analysis of twelve studies, this study examines various techniques’ types, benefits, drawbacks, and performance. The review concludes that swarm-based approaches, such as particle swarm optimization and ant colony optimization, excel in efficient search space exploration. Genetic-based techniques, such as genetic algorithms, provide further advancements. The comparison study sheds light on each approach’s unique benefits and drawbacks, offering researchers and NLP practitioners insightful information. The study concludes that swarm-based methods are efficient for rapid convergence and continuous optimization, while genetic-based methods offer flexibility but require more computational resources. The choice depends on the specific requirements of the text summarization task. Future investigations have to concentrate on tackling issues like scalability and dependability and investigating the possibilities of merging Swarm-Based and Genetic-Based techniques.
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