A Comparative Analysis and Implementation of Intelligent File Summarization System using LLM

In the era of rapid digitalization, the exponential growth of data has made it increasingly challenging for individuals and organizations to efficiently extract meaningful insights from large volumes of information. Reading and analysing extensive documents is both time-consuming and cognitively demanding. To address this issue, this paper presents an AI-Powered Intelligent File Summarization System using Large Language Models (LLMs), designed to automatically generate concise, coherent, and contextually relevant summaries from diverse file formats. The proposed system leverages advanced natural language processing techniques to transform unstructured data into simplified and consolidated information, enabling users to quickly grasp key points and make informed decisions. Unlike traditional summarization approaches, which often suffer from limited efficiency, lack of contextual understanding, and issues such as hallucination or overly generalized outputs, the proposed model incorporates improved prompt engineering and contextual filtering mechanisms to enhance accuracy and relevance. Furthermore, the system is capable of adapting to different domains and user requirements, ensuring flexibility and scalability. Experimental observations indicate that the use of LLMs significantly improves the quality of summaries while reducing information overload. The proposed solution aims to bridge the gap between raw data and actionable knowledge, offering a reliable and efficient tool for modern information processing tasks.