# SumGraph: Video Summarization via Recursive Graph Modeling

arXiv: https://arxiv.org/abs/2007.08809 · HTML: https://mlsanity.com/paper/2007.08809/

## Key facts

- Cited by 32 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Jul 2020.

## Built on by (most cited first, 30 of 32)

- [CLIP-It! Language-Guided Video Summarization](https://mlsanity.com/paper/2107.00650/) (2021)
- [Knowing Where to Focus: Event-aware Transformer for Video Grounding](https://mlsanity.com/paper/2308.06947/) (2023)
- [Align and Attend: Multimodal Summarization with Dual Contrastive Losses](https://mlsanity.com/paper/2303.07284/) (2023)
- [V2Xum-LLM: Cross-Modal Video Summarization with Temporal Prompt Instruction Tuning](https://mlsanity.com/paper/2404.12353/) (2024)
- [VideoXum: Cross-modal Visual and Textural Summarization of Videos](https://mlsanity.com/paper/2303.12060/) (2023)
- [TL;DW? Summarizing Instructional Videos with Task Relevance & Cross-Modal Saliency](https://mlsanity.com/paper/2208.06773/) (2022)
- [Looking into Your Speech: Learning Cross-modal Affinity for Audio-visual Speech Separation](https://mlsanity.com/paper/2104.02775/) (2021)
- [Language-free Training for Zero-shot Video Grounding](https://mlsanity.com/paper/2210.12977/) (2022)
- [Progressive Video Summarization via Multimodal Self-supervised Learning](https://mlsanity.com/paper/2201.02494/) (2022)
- [IntentVizor: Towards Generic Query Guided Interactive Video Summarization](https://mlsanity.com/paper/2109.14834/) (2021)
- [RA-GCN: Graph Convolutional Network for Disease Prediction Problems with Imbalanced Data](https://mlsanity.com/paper/2103.00221/) (2021)
- [MMSum: A Dataset for Multimodal Summarization and Thumbnail Generation of Videos](https://mlsanity.com/paper/2306.04216/) (2023)
- [Video Joint Modelling Based on Hierarchical Transformer for Co-summarization](https://mlsanity.com/paper/2112.13478/) (2021)
- [Towards Automated Movie Trailer Generation](https://mlsanity.com/paper/2404.03477/) (2024)
- [Video Summarization Overview](https://mlsanity.com/paper/2210.11707/) (2022)
- [Which Viewpoint Shows it Best? Language for Weakly Supervising View Selection in Multi-view Instructional Videos](https://mlsanity.com/paper/2411.08753/) (2024)
- [Contrastive Losses Are Natural Criteria for Unsupervised Video Summarization](https://mlsanity.com/paper/2211.10056/) (2022)
- [Spiking Variational Graph Representation Inference for Video Summarization](https://mlsanity.com/paper/2508.15389/) (2025)
- [Multimodal Frame-Scoring Transformer for Video Summarization](https://mlsanity.com/paper/2207.01814/) (2022)
- [VideoSAGE: Video Summarization with Graph Representation Learning](https://mlsanity.com/paper/2404.10539/) (2024)
- [ERA: Entity Relationship Aware Video Summarization with Wasserstein GAN](https://mlsanity.com/paper/2109.02625/) (2021)
- [HierSum: A Global and Local Attention Mechanism for Video Summarization](https://mlsanity.com/paper/2504.18689/) (2025)
- [Language-Guided Graph Representation Learning for Video Summarization](https://mlsanity.com/paper/2511.10953/) (2025)
- [MHSCNet: A Multimodal Hierarchical Shot-aware Convolutional Network for Video Summarization](https://mlsanity.com/paper/2204.08352/) (2022)
- [Masked Autoencoder for Unsupervised Video Summarization](https://mlsanity.com/paper/2306.01395/) (2023)
- [TripleSumm: Adaptive Triple-Modality Fusion for Video Summarization](https://mlsanity.com/paper/2603.01169/) (2026)
- [Exploring Global Diversity and Local Context for Video Summarization](https://mlsanity.com/paper/2201.11345/) (2022)
- [FastPerson: Enhancing Video Learning through Effective Video Summarization that Preserves Linguistic and Visual Contexts](https://mlsanity.com/paper/2403.17727/) (2024)
- [SELF-VS: Self-supervised Encoding Learning For Video Summarization](https://mlsanity.com/paper/2303.15993/) (2023)
- [Does SpatioTemporal information benefit Two video summarization benchmarks?](https://mlsanity.com/paper/2410.03323/) (2024)

## Abstract (arXiv)

The goal of video summarization is to select keyframes that are visually diverse and can represent a whole story of an input video. State-of-the-art approaches for video summarization have mostly regarded the task as a frame-wise keyframe selection problem by aggregating all frames with equal weight. However, to find informative parts of the video, it is necessary to consider how all the frames of the video are related to each other. To this end, we cast video summarization as a graph modeling problem. We propose recursive graph modeling networks for video summarization, termed SumGraph, to represent a relation graph, where frames are regarded as nodes and nodes are connected by semantic relationships among frames. Our networks accomplish this through a recursive approach to refine an initially estimated graph to correctly classify each node as a keyframe by reasoning the graph representation via graph convolutional networks. To leverage SumGraph in a more practical environment, we also present a way to adapt our graph modeling in an unsupervised fashion. With SumGraph, we achieved state-of-the-art performance on several benchmarks for video summarization in both supervised and unsupervised manners.