Commentary Generation for Soccer Highlights

TL;DR

This paper extends neural commentary generation models to soccer highlights, evaluating their performance on the GOAL dataset and analyzing factors affecting their accuracy and generalization.

Contribution

It adapts the MatchVoice model for short soccer highlight clips, provides extensive experimental analysis, and investigates the impact of training configurations and window sizes.

Findings

MatchVoice shows promising generalization to soccer highlights

Training configurations significantly affect performance

Varying window sizes impacts zero-shot capabilities

Abstract

Automated soccer commentary generation has evolved from template-based systems to advanced neural architectures, aiming to produce real-time descriptions of sports events. While frameworks like SoccerNet-Caption laid foundational work, their inability to achieve fine-grained alignment between video content and commentary remains a significant challenge. Recent efforts such as MatchTime, with its MatchVoice model, address this issue through coarse and fine-grained alignment techniques, achieving improved temporal synchronization. In this paper, we extend MatchVoice to commentary generation for soccer highlights using the GOAL dataset, which emphasizes short clips over entire games. We conduct extensive experiments to reproduce the original MatchTime results and evaluate our setup, highlighting the impact of different training configurations and hardware limitations. Furthermore, we explore the effect of varying window sizes on zero-shot performance. While MatchVoice exhibits promising generalization capabilities, our findings suggest the need for integrating techniques from broader video-language domains to further enhance performance. Our code is available at https://github.com/chidaksh/SoccerCommentary.