Applied Linguistics with Mixed Leader Optimizer Based English Text Summarization Model

The term ‘executed linguistics’ corresponds to an interdisciplinary domain in which the solutions are identified and provided for real-time language-related problems. exponential generation of text data on Internet must be leveraged gain knowledgeable insights. extraction meaningful insights from is crucial since it can provide value-added business organizations end-users. Automatic Text Summarization (ATS) process reduces primary size without losing any basic components data. current study introduces Applied Linguistics-based English using a Mixed Leader-Based Optimizer with Deep Learning (ALTS-MLODL) model. presented ALTS-MLODL technique aims summarize documents language. To accomplish this objective, proposed pre-processes input primarily extracts set features. Next, MLO algorithm used effectual selection extracted For summarization process, Cascaded Recurrent Neural Network (CRNN) model exploited whereas Whale Optimization Algorithm (WOA) as hyperparameter optimizer. exploitation MLO-based feature WOA-based tuning enhanced results. validate performance technique, numerous simulation analyses were conducted. experimental results signify superiority over other approaches.