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home / papers / 2026 / KCVR: Knowledge-Centric Video Reconstruction for Structured Pedagogical Summarization via Dynamic Graph Planning

KCVR: Knowledge-Centric Video Reconstruction for Structured Pedagogical Summarization via Dynamic Graph Planning

Paper · 2026 · Jingjiang Liu, Jia Zhu, Hanghui Guo et al.

Summary

Standard video summarizers aim at quick overviews. On lecture videos they often scramble the teaching logic, for example stating a proof before the definition it relies on. The authors define a new task, Structure-Pedagogical Reconstruction (SPR): producing a structured summary whose concept order follows prerequisites and is backed by what appears on the blackboard.

Their system, KCVR, plans first and writes second. A transcript-based parser (P³) divides the lecture into teaching phases. The SG-Planner then trims a Dual-Layer Epistemic Graph, removing concepts the video does not support. Generation uses two mechanisms:

  • KGVA links each active concept to blackboard regions.
  • ACP blocks concepts that should come later in the plan.

The authors also release EduStruct, a benchmark spanning 10 disciplines, together with structure-oriented metrics (KPC, LOC). KCVR beats end-to-end Video-LLM baselines and stays robust when the transcript is noisy or missing. This suggests that explicit structural priors help make educational summaries trustworthy.

Key points

  • EduStruct benchmark: more than 460 videos across 10 disciplines, paired with graphs that separate the curriculum layer (TeachingPoint) from the concept layer (KnowledgeConcept). Edges include NEXT, BASED_ON, PREREQ_OF and HAS_CONCEPT.
  • Noisy transcripts: under ASR noise, KCVR scores 61.9 KPC / 59.3 LOC, compared with 54.9 / 46.0 for InternVL3 and 57.8 / 50.5 for Text-RAG. This is only a 7.5% drop from in-domain performance.
  • Visual-only input (8 frames): KCVR keeps 55.5 KPC, against 42.0 for InternVL3. The robustness gains also appear on a VideoLLaMA2 backbone.
  • Planning is the bottleneck: substituting an oracle plan adds +3.41 KPC and +2.99 LOC. The planner itself adds less than 5% latency.
  • Domains without a graph: on InfoTech without a KG, KPC falls from 66.79 to 59.67, about a 10.7% drop.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Institutions

 Fields, topics and concepts

📚 Shelves 2The field of science this work belongs to (OpenAlex fields).Computer Science · 16,667 worksComputer Vision and Pattern Recognition · 2,869 works

🧭 Topics 1Research subjects and languages the work is about.Summarization · 232 works

Cite

Cite as Jingjiang Liu, Jia Zhu, Hanghui Guo, Weijie Shi, Yue Cui, Xiaokang Jin, Yilin Wang, Qingyu Niu, Jiawei Shen, Guoqing Ma, Yidan Liang, Shimin Di and Jiajie Xu. 2026. KCVR: Knowledge-Centric Video Reconstruction for Structured Pedagogical Summarization via Dynamic Graph Planning. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers).

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2026.acl-long.414

@inproceedings{liu2026kcvr,
  title = {{KCVR: Knowledge-Centric Video Reconstruction for Structured Pedagogical Summarization via Dynamic Graph Planning}},
  author = {Jingjiang Liu, Jia Zhu, Hanghui Guo, Weijie Shi, Yue Cui, Xiaokang Jin, Yilin Wang, Qingyu Niu, Jiawei Shen, Guoqing Ma, Yidan Liang, Shimin Di and Jiajie Xu},
  booktitle = {Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  year = {2026},
  doi = {10.18653/v1/2026.acl-long.414},
  url = {https://doi.org/10.18653/v1/2026.acl-long.414}
}

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