Multimedia Source Integration Using Specialized LLMs for News Generation

抄録

Automated journalism is emerging as an efficient means of delivering news in our increasingly AI-driven world, yet current systems remain limited by their reliance on single-modal inputs and the need for extensive human oversight. This often results in articles that lack the rich contextual detail and accuracy expected in today’s dynamic media landscape. To address this shortcoming, our research proposes a novel approach that harnesses both visual and auditory data from raw video inputs to generate comprehensive and engaging news narratives. By integrating multiple data modalities in a unified framework, our method aims to capture a fuller picture of events, thereby enhancing factual accuracy and overall quality while reducing the necessity for manual intervention. This work represents a significant step toward scalable, robust, and fully automated news production systems that meet the evolving demands of modern journalism.