Synergistic Multi-Document Text Summarization via Fuzzy Logic and Whale Optimization-Based Hybridization

With the exponential rise in digital information, the need for an effective multi-document text summarization technique which can provide concise, informative, coherent and reduce redundant information is growing increasingly. The conventional extractive summarization methods are used separately to generate the sentences maximization or sentence ranking based on statistical methods, which is hard to get the accurate estimation about the importance of sentences and to set suitable weights for the features that determine it. This paper aims to overcome these drawbacks and introduces a hybrid Fuzzy–Whale Optimization Algorithm (F-WOA) model for extractive multi-document text summarization. The Whale Optimization Algorithm (WOA) is used to optimize the weights of the extracted features, and the Mamdani Fuzzy Inference System (MFIS) uses the weighted features to estimate the importance of each sentence. First, all the input documents are preprocessed, and eight linguistic and statistical features are extracted. Each candidate weight vector is evaluated by passing the weighted features through the MFIS and measuring the topic relevance, cohesion, and readability of the resulting summary. The best weights are then used by the MFIS to score the sentences, and the top-ranked non-redundant sentences form the final summary. The experiments were performed on the benchmark datasets DUC-2002, DUC-2004, DUC-2006, and DUC-2007 with the ROUGE-1, ROUGE-2, and ROUGE-L evaluation metrics. Experimental results achieved ROUGE-1, ROUGE-2, and ROUGE-L F-scores of up to 50.79, 25.60, and 31.75, respectively. Comparative analysis shows that the integration of fuzzy logic into WOA enhances the quality of the summaries compared to the WOA-only variant that scores sentence with a weighted sum of the features.