Overview

Introducing V2Xum-LLM, an innovative framework geared towards enabling effective and efficient summarization across different modalities using a large language model. The approach integrates advanced techniques like temporal prompts to achieve alignment between video and text summary generation, offering a comprehensive solution for multimodal video summarization tasks.

Main Contributions

• Integration of diverse modalities: Facilitates synchronization between video and textual summary elements, enhancing the comprehensibility of summarized content.
• Temporal prompts for refinement: Uses temporal prompts to refine the summarization process, ensuring relevance and conciseness.
• Benchmark dataset Instruct-V2Xum: The introduction of a new dataset that includes a large collection of videos paired with textual summaries enhances research and application potential.

Importance

This work is pivotal for the development of multimodal summarization technologies, significantly impacting areas such as content creation, media, and education. The framework’s flexibility and its ability to handle complex summarization tasks provide a solid foundation for future enhancements and broader applications.