As video content continues to expand across educational platforms, recorded lectures, and live-streamed entertainment, the need for efficient and structured analysis of long-form footage has increased. Although many existing AI programs provide high-level video summaries based on AI-generated transcripts, these approaches are often limited to coarse overviews and lack detailed analysis of a video's structure, thematic progression, and semantic relationships, all of which are required for comprehensive video analysis.
This paper proposes an LLM-based video summarization framework that balances macro-level comprehension with micro-level semantic analysis. The first stage of the process indexes the video at a micro level by:
- Analyzing the full transcript;
- Analyzing individual transcript sentences;
- Grouping these sentences by semantic similarity using an LLM as a judge.
Contextual continuity is retained during sentence-level processing by incorporating both the global transcript analysis and adjacent sentence information into each evaluation prompt. This framework establishes a foundation for video analysis tools that visualize semantic chunking and semantic matching through relevance-based heatmaps. Limitations and future expansions of the framework are also discussed.
Blogger's Review: The Scribby framework effectively addresses the complexity of video content, enhancing the quality of video summaries through multi-level semantic analysis. This not only provides new tools for the education and entertainment sectors but also lays an important foundation for future AI video analysis. Its meticulous handling of context is particularly noteworthy, and I look forward to seeing its practical applications and developments.