(untitled) (International Journal of Latest Technology in Engineering Management & Applied Science, 2025)
The exponential growth of governmental documentation in India has created an urgent need for efficient automated summarization systems. This research presents eSummarizer AI Service, a custom Transformer-based machine learning model designed specifically for summarizing Indian government documents such as policy papers, circulars, legislative texts, and departmental reports. The study employs advanced Natural Language Processing (NLP) techniques, including BART (Bidirectional and Auto-Regressive Transformer), reinforcement learning, and a custom dataset curated from official government portals. Evaluation using ROUGE metrics demonstrates significant improvements over existing baseline models, achieving high coherence, contextual relevance, and factual consistency. The proposed system has practical applications for policymakers, researchers, and citizens, enhancing the accessibility and comprehension of complex governmental information.