🤖 AI Summary
This work addresses the limitations of existing video analysis methods, which rely on coarse-grained AI summaries and struggle to capture the structural evolution and semantic relationships in long-form videos. To overcome this, the authors propose a multi-level large language model (LLM) framework that first performs global semantic modeling over full video transcripts, then conducts context-aware sentence-by-sentence parsing. Innovatively, the framework employs an LLM as a discriminator to cluster sentences based on semantic similarity, enabling fine-grained semantic segmentation. By integrating both global and local contextual information, the approach supports the generation of interpretable visualizations—including semantic structure graphs and relevance heatmaps—thereby significantly enhancing the depth and explainability of video understanding. This method is particularly well-suited for applications such as educational content analysis and lecture replay, where structured semantic insight is essential.
📝 Abstract
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 \cite{1}. Although many existing AI programs provide high-level video summaries based on AI-generated transcripts \cite{2,3,4,5}, 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 \cite{6,12,13}. The first stage of the process indexes the video at a micro level by (1) analyzing the full transcript, (2) analyzing individual transcript sentences, and (3) grouping these sentences by semantic similarity using an LLM as a judge \cite{6,13}. 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.