IMPROVING EXTRACTIVE TEXT SUMMARIZATION VIA EFFICIENT COATI ALGORITHM FOR SINGLE DOCUMENT
Abstract
In the digital era, the rapid expansion of online information demands efficient automated text summarization techniques to extract key insights from large documents. This study introduces a novel single-document extractive summarization approach that utilizes Term Frequency-Inverse Topic Frequency (TF-ITF) for feature extraction and the Coati Optimization Algorithm (COA) for optimal sentence selection. COA enhances summarization performance by balancing precision and recall through an adaptive fitness function, improving the quality of extracted summaries. The proposed model is evaluated on DUC 2002, 2003, and 2005 datasets using ROUGE, BLEU, precision, recall, and F1-score metrics. Comparative analysis against state-of-the-art optimization algorithms, including PSO, CSO, GWO, BCO, QABC, MCSO, and GLO, demonstrates that COA outperforms existing techniques, achieving higher recall and F1 scores while maintaining competitive precision. These findings establish COA as an effective optimization technique for enhancing automated text summarization.
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