Visual and Memory-Augmented Soccer Commentary Generation

Automatic soccer commentary generation aims to bridge the gap between raw visual content and professional, tactical commentary. However, existing datasets tend to produce incomplete commentary that lacks semantic richness and fails to convey the full visual information present in standard video clips. To address these limitations, we propose two manually curated datasets: SN-Short, which enhances scene-level semantic descriptions, and SN-Long, which captures event continuity for context-aware commentary. Based on these, we design a commentary augmentation pipeline that transforms incomplete annotations into MatchText, a semantically complete and structurally standardized dataset. Leveraging this supervision, we introduce MatchAware, a generation model that incorporates contextual cues from previous events to produce coherent commentary aligned with the visual flow of the game. Experimental results show that proposed approach significantly outperforms existing baselines on the constructed datasets. 45: 03 42: 14 SN-Caption (Mkhallati et al.2023): Dwight Gayle (Crystal Palace) launches a cross from the corner, but David Ospina is alert to thwart the effort. SN-Short (ours): Dwight Gayle (Crystal Palace) launches a cross from the corner, but David Ospina is alert to thwart the effort. The cross was aimed at the far post, but the keeper stood firm and cleared the danger. SN-Long (ours): ・・・・・・ SN-Caption (Mkhallati et al.2023): Goal! Olivier Giroud (Arsenal) fires the rebound inside the right post after the ball breaks to him in the box. The score is 0:2. SN-Short (ours): Goal! Olivier Giroud (Arsenal) fires the rebound inside the right post after the ball breaks to him in the box. The score is 0:2. The away fans erupt in joy, celebrating the crucial goal just before halftime. SN-Long (ours): SN-Short + Both sides have been creating scoring opportunities, with the keepers making key saves at both ends. But it's the away team who made theirs count.