Innovative abstractive Hindi text summarization model incorporating Bi-LSTM classifier, optimizer and generative AI
Babita Verma, Ani Thomas, Rohit Kumar Verma · Intelligent Decision Technologies · 2025
This research explores the development and optimization of an advanced abstractive text summarization model specifically tailored for the Hindi language. The proposed model leverages the Cetacean Predator Optimization-Based Sentence Rank BiLSTM model (CPO-BiLSTM) as its core architecture, enhancing the model's ability to capture intricate dependencies in both forward and backward directions. BiLSTMs are versatile architectures that can be applied to various tasks utilising generative AI, especially ones that incorporate sequential data like speech, text, or music. The study focuses on optimizing the BiLSTM classifier through various optimization techniques to improve the quality and coherence of generated summaries. The optimization strategies employed in this research are guided by the objective of achieving superior performance in terms of both informativeness and linguistic quality. The study conducts comprehensive experiments on diverse datasets to evaluate the proposed model's effectiveness in capturing key information and generating concise, coherent, and contextually relevant summaries in the Hindi language. The results demonstrate the potential of the optimized abstractive Hindi text summarization BiLSTM classifier model and Leveraging Generative AI techniques, our model generates concise and informative summaries that effectively capture the salient points of the input text. This research adds to the expanding corpus of knowledge in natural language processing and offers insightful information for applications that need Hindi text to be automatically summarised.