# SoccerHigh: A Benchmark Dataset for Automatic Soccer Video Summarization

arXiv: https://arxiv.org/abs/2509.01439 · HTML: https://mlsanity.com/paper/2509.01439/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 49 references that are in the map.
- First posted on arXiv 1 Sep 2025.

## Builds on (most cited first, 21 of 21)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [DINOv2: Learning Robust Visual Features without Supervision](https://mlsanity.com/paper/2304.07193/) (2023)
- [Emerging Properties in Self-Supervised Vision Transformers](https://mlsanity.com/paper/2104.14294/) (2021)
- [mixup: Beyond Empirical Risk Minimization](https://mlsanity.com/paper/1710.09412/) (2017)
- [Do ImageNet Classifiers Generalize to ImageNet?](https://mlsanity.com/paper/1902.10811/) (2019)
- [HowTo100M: Learning a Text-Video Embedding by Watching Hundred Million Narrated Video Clips](https://mlsanity.com/paper/1906.03327/) (2019)
- [DataComp: In search of the next generation of multimodal datasets](https://mlsanity.com/paper/2304.14108/) (2023)
- [TransNet V2: An effective deep network architecture for fast shot transition detection](https://mlsanity.com/paper/2008.04838/) (2020)
- [SoccerNet: A Scalable Dataset for Action Spotting in Soccer Videos](https://mlsanity.com/paper/1804.04527/) (2018)
- [SoccerNet-v2: A Dataset and Benchmarks for Holistic Understanding of Broadcast Soccer Videos](https://mlsanity.com/paper/2011.13367/) (2020)
- [CLIP-It! Language-Guided Video Summarization](https://mlsanity.com/paper/2107.00650/) (2021)
- [QVHighlights: Detecting Moments and Highlights in Videos via Natural Language Queries](https://mlsanity.com/paper/2107.09609/) (2021)
- [Align and Attend: Multimodal Summarization with Dual Contrastive Losses](https://mlsanity.com/paper/2303.07284/) (2023)
- [Video Summarization with Attention-Based Encoder-Decoder Networks](https://mlsanity.com/paper/1708.09545/) (2017)
- [MH-DETR: Video Moment and Highlight Detection with Cross-modal Transformer](https://mlsanity.com/paper/2305.00355/) (2023)
- [Exploring global diverse attention via pairwise temporal relation for video summarization](https://mlsanity.com/paper/2009.10942/) (2020)
- [Title Generation for User Generated Videos](https://mlsanity.com/paper/1608.07068/) (2016)
- [A Graph-Based Method for Soccer Action Spotting Using Unsupervised Player Classification](https://mlsanity.com/paper/2211.12334/) (2022)
- [CFSum: A Transformer-Based Multi-Modal Video Summarization Framework With Coarse-Fine Fusion](https://mlsanity.com/paper/2503.00364/) (2025)
- [A Multi-stage deep architecture for summary generation of soccer videos](https://mlsanity.com/paper/2205.00694/) (2022)
- [Learning to Summarize Videos by Contrasting Clips](https://mlsanity.com/paper/2301.05213/) (2023)

## Built on by (most cited first, 3 of 3)

- [RefereeBench: Are Video MLLMs Ready to be Multi-Sport Referees](https://mlsanity.com/paper/2604.15736/) (2026)
- [Where is the multimodal goal post? On the Ability of Foundation Models to Recognize Highlights in Videos](https://mlsanity.com/paper/2601.16333/) (2026)
- [Audio for Sports Highlight Detection: A Comparative Empirical Study](https://mlsanity.com/paper/2609.17923/) (2026)

## Abstract (arXiv)

Video summarization aims to extract key shots from longer videos to produce concise and informative summaries. One of its most common applications is in sports, where highlight reels capture the most important moments of a game, along with notable reactions and specific contextual events. Automatic summary generation can support video editors in the sports media industry by reducing the time and effort required to identify key segments. However, the lack of publicly available datasets poses a challenge in developing robust models for sports highlight generation. In this paper, we address this gap by introducing a curated dataset for soccer video summarization, designed to serve as a benchmark for the task. The dataset includes shot boundaries for 237 matches from the Spanish, French, and Italian leagues, using broadcast footage sourced from the SoccerNet dataset. Alongside the dataset, we propose a baseline model specifically designed for this task, which achieves an F1 score of 0.3956 in the test set. Furthermore, we propose a new metric constrained by the length of each target summary, enabling a more objective evaluation of the generated content. The dataset and code are available at https://ipcv.github.io/SoccerHigh/.