A Framework for Analyzing and Summarizing News and Articles using Large Language Model

This research presents the development of a chatbot that utilizes Latent Semantic Analysis (LSA) and Large Language Models (LLMs) to effectively summarize news articles from multiple sources. By analyzing and synthesizing information from various sources, the chatbot provides users with concise and informative summaries of complex news events. This eliminates the need for users to manually collect and organize information from multiple sources, saving time and effort. The chatbot effectively removes irrelevant information and presents the summarized information in a clear and concise format, enhancing user understanding and comprehension. This innovative approach has the potential to revolutionize news consumption and enhance information dissemination in the digital age.