Towards Automatic Soccer Commentary Generation with Knowledge-Enhanced Visual Reasoning
Summary: arXiv:2604.00057v1
Type: Cross
Abstract
Soccer commentary plays a crucial role in enhancing the soccer game viewing experience for audiences. Previous studies in automatic soccer commentary generation typically adopt an end-to-end method to generate anonymous live text commentary. Such generated commentary is insufficient in the context of real-world live televised commentary, as it contains anonymous entities, context-dependent errors, and lacks statistical insights of the game events.
Introduction
To bridge the gap between current commentary generation methods and the needs of live televised broadcasts, we propose GameSight, a two-stage model designed to address soccer commentary generation as a knowledge-enhanced visual reasoning task. This innovative approach aims to enable a more knowledgeable and engaging commentary experience that accurately references entities such as players and teams.
Methodology
GameSight operates in two distinct stages:
- Visual Reasoning: The first stage involves aligning anonymous entities with fine-grained visual and contextual analysis. This step ensures that the commentary generated is more relevant and context-aware.
- Knowledge Refinement: The second stage refines the entity-aligned commentary by incorporating external historical statistics and iteratively updating internal game state information. This knowledge enhancement allows for richer, more informative commentary.
Results
Our model significantly improves the player alignment accuracy by 18.5% on the SN-Caption-test-align dataset compared to the existing model, Gemini 2.5-pro. Furthermore, GameSight has shown enhancements in multiple areas:
- Segment-level accuracy
- Commentary quality
- Game-level contextual relevance
- Structural composition
Conclusion
We believe that GameSight represents a significant advancement in the field of automatic soccer commentary generation. By focusing on knowledge-enhanced visual reasoning, our work paves the way for a more informative and engaging human-centric experience in AI sports applications. As artificial intelligence continues to evolve, the potential for creating dynamic and context-aware commentary will enhance the spectator experience in unprecedented ways.
Demo Page
For a practical demonstration of GameSight, visit our demo page at: GameSight Demo.