Bharathi Mohan G ^1* Altalbe Ali ^2,3* Prasanna Kumar R ^1*

• ¹ Department of Computer Science and Engineering, Amrita School of Computing, Amrita Vishwa Vidyapeetham Chennai, Chennai, India
• ² Prince Sattam Bin Abdulaziz University, Al-Kharj, Saudi Arabia
• ³ Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Makkah, Saudi Arabia

Automatic text summarization is a cornerstone of natural language processing, yet existing methods often struggle to maintain contextual integrity and capture nuanced sentence relationships. Introducing the Optimized Auto Encoded Long Short-Term Memory Network (OAELSTM), enhanced by the Whale Optimization Algorithm (WOA), offers a novel approach to this challenge. Existing summarization models frequently produce summaries that are either too generic or disjointed, failing to preserve the essential content. The OAELSTM model, integrating deep LSTM layers and autoencoder mechanisms, focuses on extracting key phrases and concepts, ensuring that summaries are both informative and coherent. WOA fine-tunes the model's parameters, enhancing its precision and efficiency. Evaluation on datasets like CNN/Daily Mail and Gigaword demonstrates the model's superiority over existing approaches. It achieves a ROUGE Score of 0.456, an accuracy rate of 84.47%, and a specificity score of 0.3244, all within an efficient processing time of 4341.95 seconds.