# A Multi-stage deep architecture for summary generation of soccer videos

arXiv: https://arxiv.org/abs/2205.00694 · HTML: https://mlsanity.com/paper/2205.00694/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 34 of its 102 references that are in the map.
- First posted on arXiv 2 May 2022.

## Builds on (most cited first, 30 of 34)

- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Fast R-CNN](https://mlsanity.com/paper/1504.08083/) (2015)
- [Attention-based Deep Multiple Instance Learning](https://mlsanity.com/paper/1802.04712/) (2018)
- [R-C3D: Region Convolutional 3D Network for Temporal Activity Detection](https://mlsanity.com/paper/1703.07814/) (2017)
- [SoccerNet: A Scalable Dataset for Action Spotting in Soccer Videos](https://mlsanity.com/paper/1804.04527/) (2018)
- [Video Summarization with Long Short-term Memory](https://mlsanity.com/paper/1605.08110/) (2016)
- [SoccerNet-v2: A Dataset and Benchmarks for Holistic Understanding of Broadcast Soccer Videos](https://mlsanity.com/paper/2011.13367/) (2020)
- [Revisiting Multiple Instance Neural Networks](https://mlsanity.com/paper/1610.02501/) (2016)
- [Multiple Instance Detection Network with Online Instance Classifier Refinement](https://mlsanity.com/paper/1704.00138/) (2017)
- [TURN TAP: Temporal Unit Regression Network for Temporal Action Proposals](https://mlsanity.com/paper/1703.06189/) (2017)
- [Stochastic Optimization of Sorting Networks via Continuous Relaxations](https://mlsanity.com/paper/1903.08850/) (2019)
- [Deep Reinforcement Learning for Unsupervised Video Summarization with Diversity-Representativeness Reward](https://mlsanity.com/paper/1801.00054/) (2017)
- [Actions Speak Louder Than Goals: Valuing Player Actions in Soccer](https://mlsanity.com/paper/1802.07127/) (2018)
- [Video Summarization Using Deep Neural Networks: A Survey](https://mlsanity.com/paper/2101.06072/) (2021)
- [Attention-Based Multimodal Fusion for Video Description](https://mlsanity.com/paper/1701.03126/) (2017)
- [Video Summarization Using Fully Convolutional Sequence Networks](https://mlsanity.com/paper/1805.10538/) (2018)
- [HyperNet: Towards Accurate Region Proposal Generation and Joint Object Detection](https://mlsanity.com/paper/1604.00600/) (2016)
- [Hierarchical Recurrent Neural Network for Video Summarization](https://mlsanity.com/paper/1904.12251/) (2019)
- [Weakly Supervised Object Localization with Multi-fold Multiple Instance Learning](https://mlsanity.com/paper/1503.00949/) (2015)
- [Temporal Context Network for Activity Localization in Videos](https://mlsanity.com/paper/1708.02349/) (2017)
- [Video Summarization by Learning from Unpaired Data](https://mlsanity.com/paper/1805.12174/) (2018)
- [What Actions are Needed for Understanding Human Actions in Videos?](https://mlsanity.com/paper/1708.02696/) (2017)
- [Summary Transfer: Exemplar-based Subset Selection for Video Summarization](https://mlsanity.com/paper/1603.03369/) (2016)
- [Deep Reinforcement Learning in Ice Hockey for Context-Aware Player Evaluation](https://mlsanity.com/paper/1805.11088/) (2018)
- [Multimodal Attention for Neural Machine Translation](https://mlsanity.com/paper/1609.03976/) (2016)
- [Collaborative Summarization of Topic-Related Videos](https://mlsanity.com/paper/1706.03114/) (2017)
- [A General Framework for Edited Video and Raw Video Summarization](https://mlsanity.com/paper/1904.10669/) (2019)
- [Leveraging Contextual Cues for Generating Basketball Highlights](https://mlsanity.com/paper/1606.08955/) (2016)
- [PlayeRank: data-driven performance evaluation and player ranking in soccer via a machine learning approach](https://mlsanity.com/paper/1802.04987/) (2018)
- [Video Summarization via Actionness Ranking](https://mlsanity.com/paper/1903.00110/) (2019)

## Built on by (most cited first, 1 of 1)

- [SoccerHigh: A Benchmark Dataset for Automatic Soccer Video Summarization](https://mlsanity.com/paper/2509.01439/) (2025)

## Abstract (arXiv)

Video content is present in an ever-increasing number of fields, both scientific and commercial. Sports, particularly soccer, is one of the industries that has invested the most in the field of video analytics, due to the massive popularity of the game and the emergence of new markets. Previous state-of-the-art methods on soccer matches video summarization rely on handcrafted heuristics to generate summaries which are poorly generalizable, but these works have yet proven that multiple modalities help detect the best actions of the game. On the other hand, machine learning models with higher generalization potential have entered the field of summarization of general-purpose videos, offering several deep learning approaches. However, most of them exploit content specificities that are not appropriate for sport whole-match videos. Although video content has been for many years the main source for automatizing knowledge extraction in soccer, the data that records all the events happening on the field has become lately very important in sports analytics, since this event data provides richer context information and requires less processing. We propose a method to generate the summary of a soccer match exploiting both the audio and the event metadata. The results show that our method can detect the actions of the match, identify which of these actions should belong to the summary and then propose multiple candidate summaries which are similar enough but with relevant variability to provide different options to the final editor. Furthermore, we show the generalization capability of our work since it can transfer knowledge between datasets from different broadcasting companies, different competitions, acquired in different conditions, and corresponding to summaries of different lengths