Improved Bi-GRU framework for Multi-document text summarization with aspect and thematic feature descriptor: Model training via hybrid optimization

Abstract

Text summarization condenses a text into its essential points for a quick grasp of the main ideas. Multi-document summarization integrates information from several sources to provide a comprehensive overview. Techniques include extractive methods, which select key sentences, and abstractive methods, which generate new sentences. Hybrid methods combine these approaches to improve summary quality. Limitations include challenges in maintaining coherence, context, and nuance. Further improvements are needed to enhance coherence, accuracy, and comprehensiveness in summaries. To address these issues, this research proposes the Improved Bidirectional Gated Recurrent Unit (IBi-GRU) model for Multi-document Text Summarization through COOT optimization updated Coati Optimization Algorithm (CuCOA). The process involves preprocessing, feature extraction, and summarization. Initially, tokenization is performed during the preprocessing. Pertinent features are then extracted from the preprocessed text in the phase of feature extraction, followed by summarization using the IBi-GRU model with its weight parameters optimally tuned by the CuCOA approach. Comprehensive simulations and experimental assessments in terms of accuracy, Mathews Correlation Coefficient (MCC), False Negative Rate (FNR), etc., validate the IBi-GRU model. This demonstrates its robustness and potential for various text summarization applications in comparison with conventional approaches. The CuCOA + IBi-GRU scheme achieved the highest scores, with a Rouge of 0.866, Precision of 0.887, Recall of 0.853, and F-Measure of 0.913.