CausalChapter improves chaptering for long instructional videos
CausalChapter: Improving Long-Video Chaptering with Interventional Dependency Modeling
Computer Vision and Pattern RecognitionArtificial Intelligence
Summary
Long instructional videos can be hard to navigate because they often don’t have clear chapters to separate topics. The authors propose CausalChapter, a method that helps find chapter boundaries and write chapter summaries more accurately by checking how different parts of the video influence predictions. This approach uses a clever masking technique to spot where topics change and improves linking across chapters for better descriptions. Their method works better than previous ones for videos with long, dense content and smoothly changing topics.
What this means in practice
- •For video platform developers: Automatically segment and label long educational videos to enhance user navigation and content discovery.$Commercial implications: Enables platforms to offer refined chapter browsing features that improve viewer retention and satisfaction.
- •For content management teams: Improve indexing and summarization of lecture video archives by detecting topic shifts and generating coherent chapter descriptions.
Authors
Xinran Duan, Guozhang Li, Yaoyao Zhong, Mei Wang, Lizhi Wang, Hua Huang
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 intervention-inspired framework for long-video chaptering that estimates prediction-level influence through lightweight masking and removal interventions. For boundary localization, our Local Dependency Shift module detects drops in predictive dependency between adjacent temporal windows; for chapter description generation, our Cross-Segment Support Selection module reranks historical contexts according to their support for the current prediction. Experiments on long-video chaptering benchmarks show that CausalChapter improves boundary localization, chapter description quality, and cross-chapter coherence.