CausalChapter: Improving Long-Video Chaptering with Interventional Dependency Modeling
Quick summary
arXiv:2609.08686v1 Announce Type: cross Abstract: Long-form instructional videos require automatic chaptering to support browsing, navigation, and knowledge access. Recent long-context language models can perform chaptering from textualized video inputs, but they remain costly and brittle for content-dense lecture videos with long transcripts, smooth topic transitions, and detailed chapter outputs. A scalable segment-then-caption paradigm reduces this cost, but introduces two new challenges: boundary error propagation and fragmented cross-chapter context. We propose \textbf{CausalChapter}, an
Key takeaways
- arXiv:2609.08686v1 Announce Type: cross Abstract: Long-form instructional videos require automatic chaptering to support browsing, navigation, and knowledge access.
- Recent long-context language models can perform chaptering from textualized video inputs, but they remain costly and brittle for content-dense lecture videos with long transcripts, smooth topic transitions, and detailed chapter outputs.
- A scalable segment-then-caption paradigm reduces this cost, but introduces two new challenges: boundary error propagation and fragmented cross-chapter context.
Why it matters
“CausalChapter: Improving Long-Video Chaptering with Interventional Dependency Modeling” illustrates how changes in the AI ecosystem can affect products, workflows and user expectations together. Its lasting significance depends on measurable adoption, cost and safety outcomes.
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