ARC-Chapter: Structuring Hour-Long Videos into Navigable Chapters and Hierarchical Summaries

• URL: http://arxiv.org/abs/2511.14349v1
• Date: Tue, 18 Nov 2025 10:53:14 GMT
• Title: ARC-Chapter: Structuring Hour-Long Videos into Navigable Chapters and Hierarchical Summaries
• Authors: Junfu Pu, Teng Wang, Yixiao Ge, Yuying Ge, Chen Li, Ying Shan,
• Abstract summary: ARC-Chapter is the first large-scale video chaptering model trained on over million-level long video chapters.<n>It unifies ASR transcripts, scene texts, visual captions into multi-level annotations, from short title to long summaries.<n>It establishes a new state-of-the-art by a significant margin, outperforming the previous best by 14.0% in F1 score and 11.3% in SODA score.
• Score: 77.41072125938636
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The proliferation of hour-long videos (e.g., lectures, podcasts, documentaries) has intensified demand for efficient content structuring. However, existing approaches are constrained by small-scale training with annotations that are typical short and coarse, restricting generalization to nuanced transitions in long videos. We introduce ARC-Chapter, the first large-scale video chaptering model trained on over million-level long video chapters, featuring bilingual, temporally grounded, and hierarchical chapter annotations. To achieve this goal, we curated a bilingual English-Chinese chapter dataset via a structured pipeline that unifies ASR transcripts, scene texts, visual captions into multi-level annotations, from short title to long summaries. We demonstrate clear performance improvements with data scaling, both in data volume and label intensity. Moreover, we design a new evaluation metric termed GRACE, which incorporates many-to-one segment overlaps and semantic similarity, better reflecting real-world chaptering flexibility. Extensive experiments demonstrate that ARC-Chapter establishes a new state-of-the-art by a significant margin, outperforming the previous best by 14.0% in F1 score and 11.3% in SODA score. Moreover, ARC-Chapter shows excellent transferability, improving the state-of-the-art on downstream tasks like dense video captioning on YouCook2.

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