ARC-Chapter: Structuring Hour-Long Videos into Navigable Chapters and Hierarchical Summaries
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.
691e9ebf3c64d32b03645933
Junfu Pu
claimed_verified
2025-11-20T08:48:53.327Z
646c5fcc97819a8be938ee9b
/avatars/27976d701e88aebf5855418c055ba50e.svg
Junfu Pu
Jevin754
user
Jevin754
691e9ebf3c64d32b03645934
Teng Wang
691e9ebf3c64d32b03645935
Yixiao Ge
691e9ebf3c64d32b03645936
Yuying Ge
691e9ebf3c64d32b03645937
Chen Li
691e9ebf3c64d32b03645938
Ying Shan
ARC-Chapter: Structuring Hour-Long Videos into Navigable Chapters and Hierarchical Summaries
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.
ARC-Chapter is a large-scale video chaptering model that improves performance through extensive training data and a new evaluation metric, demonstrating superior results and transferability.
video chaptering ASR transcripts scene texts visual captions multi-level annotations GRACE F1 score SODA score dense video captioning