# NBA_Streaming: A Large-Scale Benchmark for Fine-Grained Basketball Commentary Generation in Continuous Streams

arXiv: https://arxiv.org/abs/2608.09200 · HTML: https://mlsanity.com/paper/2608.09200/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 25 of its 42 references that are in the map.
- First posted on arXiv 10 Aug 2026.

## Builds on (most cited first, 25 of 25)

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- [Vid2Seq: Large-Scale Pretraining of a Visual Language Model for Dense Video Captioning](https://mlsanity.com/paper/2302.14115/) (2023)
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- [Video ReCap: Recursive Captioning of Hour-Long Videos](https://mlsanity.com/paper/2402.13250/) (2024)
- [Streaming Dense Video Captioning](https://mlsanity.com/paper/2404.01297/) (2024)
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- [SoccerNet-Caption: Dense Video Captioning for Soccer Broadcasts Commentaries](https://mlsanity.com/paper/2304.04565/) (2023)
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- [MatchTime: Towards Automatic Soccer Game Commentary Generation](https://mlsanity.com/paper/2406.18530/) (2024)
- [GOAL: A Challenging Knowledge-grounded Video Captioning Benchmark for Real-time Soccer Commentary Generation](https://mlsanity.com/paper/2303.14655/) (2023)
- [Sports Video Analysis on Large-Scale Data](https://mlsanity.com/paper/2208.04897/) (2022)
- [HAT: History-Augmented Anchor Transformer for Online Temporal Action Localization](https://mlsanity.com/paper/2408.06437/) (2024)
- [Widely Applicable Strong Baseline for Sports Ball Detection and Tracking](https://mlsanity.com/paper/2311.05237/) (2023)
- [Online Temporal Action Localization with Memory-Augmented Transformer](https://mlsanity.com/paper/2408.02957/) (2024)
- [Player-Centric Multimodal Prompt Generation for Large Language Model Based Identity-Aware Basketball Video Captioning](https://mlsanity.com/paper/2507.20163/) (2025)
- [Knowledge Guided Entity-aware Video Captioning and A Basketball Benchmark](https://mlsanity.com/paper/2401.13888/) (2024)
- [Live Video Captioning](https://mlsanity.com/paper/2406.14206/) (2024)
- [OnPoint: Offline-to-Online Multi-Level Distillation for Point-Supervised Online Temporal Action Localization](https://mlsanity.com/paper/2607.00289/) (2026)

## Abstract (arXiv)

Live basketball commentary generation requires determining when an event is sufficiently observable and describing it before subsequent events unfold. However, existing methods are primarily designed for pre-segmented clips or complete videos, making them unsuitable for continuous streams. Existing datasets also provide limited supervision for player identities, fine-grained actions, event attributes, and coherent event chains, restricting the factual richness of generated commentary. To address these limitations, we introduce NBA_Streaming, a large-scale benchmark for online fine-grained basketball commentary generation. It contains 307.5 hours of basketball broadcasts and approximately 35K temporally aligned events, with annotations of event boundaries, player identities, fine-grained actions, event chains, and natural-language commentary. By moving from isolated clips to continuous streams, NBA_Streaming enables unified evaluation of event localization, response reliability, factual grounding, and commentary quality under causal constraints. We further propose a causal two-stage framework that combines completion-first localization with ball-centric semantic grounding, enabling the system to identify complete events from observed streams and organize scene, event, identity, and action cues for commentary generation. Extensive experiments reveal the difficulty of NBA_Streaming, where existing baselines struggle with online timing, factual grounding, and fine-grained description. Our framework consistently improves over strong alternatives, while the remaining gap highlights NBA_Streaming as a valuable benchmark for streaming sports video understanding and generation. The dataset is publicly available at https://github.com/wyy-081/NBA_Streaming.