Spotted Hyena Optimization with Deep Learning-Based Automatic Text Document Summarization Model

Automatic text summarization is an active investigation region determined as removing snippets or introductory sentences of a massive document and relating them short form documents. Text Summarization can be either costefficient time-efficient. An abstractive extractive summary was studied with distinct algorithms comprising deep learning (DL), graph, statistical-based techniques. DL has attained promising shows in comparison to the typical methods. With development various neural structures like attention mechanism (usually called transformer), there potential growth area for tasks. Hence, this research presents Spotted Hyena Optimization Deep Learning based (SHODL-ATS) model. The SHODL-ATS technique's principal objective lies documents' automated summarization. To accomplish this, presented technique performs data preprocessing convert into convenient form. uses Attention-based Bidirectional Gated Recurrent Unit (ABiGRU) model summarizing Finally, SHO enforced parameter tuning ABiGRU approach. examine achievement model, we validate outcomes on benchmark datasets. results indicate method over other existing