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The rapid expansion of digital news sources has necessitated intelligent systems capable of filtering, analyzing, and deriving meaningful insights from vast amounts of information in real time. This study presents an AI-driven chatbot designed for real-time news automation, integrating advanced natural language processing techniques, knowledge graphs, and generative AI models to improve news summarization and correlation analysis. The chatbot processes over 1,306,518 news reports spanning from 25 September 2023 to 17 February 2025, categorizing them into 15 primary event categories and extracting key insights through structured analysis. By employing state-of-the-art machine learning techniques, the system enables real-time classification, interactive query-based exploration, and automated event correlation. The chatbot demonstrated high accuracy in both summarization and correlation tasks, achieving an average F1 score of 0.94 for summarization and 0.92 for correlation analysis. Summarization queries were processed within an average response time of 9 s, while correlation analyses required approximately 21 s per query. The chatbot’s ability to generate real-time, concise news summaries and uncover hidden relationships between events makes it a valuable tool for applications in disaster response, policy analysis, cybersecurity, and public communication. This research contributes to the field of AI-driven news analytics by bridging the gap between static news retrieval platforms and interactive conversational agents. Future work will focus on expanding multilingual support, enhancing misinformation detection, and optimizing computational efficiency for broader real-world applicability. The proposed chatbot stands as a scalable and adaptive solution for real-time decision support in dynamic information environments.
Sufi et al. (Tue,) studied this question.