# Leveraging Contextual Cues for Generating Basketball Highlights

arXiv: https://arxiv.org/abs/1606.08955 · HTML: https://mlsanity.com/paper/1606.08955/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Jun 2016.

## Built on by (most cited first, 10 of 10)

- [Deep Learning-based Action Detection in Untrimmed Videos: A Survey](https://mlsanity.com/paper/2110.00111/) (2021)
- [A Survey on Video Action Recognition in Sports: Datasets, Methods and Applications](https://mlsanity.com/paper/2206.01038/) (2022)
- [A Survey of Deep Learning in Sports Applications: Perception, Comprehension, and Decision](https://mlsanity.com/paper/2307.03353/) (2023)
- [Automatic Non-Linear Video Editing Transfer](https://mlsanity.com/paper/2105.06988/) (2021)
- [Tracking Skiers from the Top to the Bottom](https://mlsanity.com/paper/2312.09723/) (2023)
- [Fighting Game Commentator with Pitch and Loudness Adjustment Utilizing Highlight Cues](https://mlsanity.com/paper/2108.08112/) (2021)
- [DIAMOND: An LLM-Driven Agent for Context-Aware Baseball Highlight Summarization](https://mlsanity.com/paper/2506.02351/) (2025)
- [HierSum: A Global and Local Attention Mechanism for Video Summarization](https://mlsanity.com/paper/2504.18689/) (2025)
- [A Multi-stage deep architecture for summary generation of soccer videos](https://mlsanity.com/paper/2205.00694/) (2022)
- [Where is the multimodal goal post? On the Ability of Foundation Models to Recognize Highlights in Videos](https://mlsanity.com/paper/2601.16333/) (2026)

## Abstract (arXiv)

The massive growth of sports videos has resulted in a need for automatic generation of sports highlights that are comparable in quality to the hand-edited highlights produced by broadcasters such as ESPN. Unlike previous works that mostly use audio-visual cues derived from the video, we propose an approach that additionally leverages contextual cues derived from the environment that the game is being played in. The contextual cues provide information about the excitement levels in the game, which can be ranked and selected to automatically produce high-quality basketball highlights. We introduce a new dataset of 25 NCAA games along with their play-by-play stats and the ground-truth excitement data for each basket. We explore the informativeness of five different cues derived from the video and from the environment through user studies. Our experiments show that for our study participants, the highlights produced by our system are comparable to the ones produced by ESPN for the same games.