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From Multimodal Perception to Strategic Reasoning: A Survey on AI-Generated Game Commentary

Qirui Zheng, Xingbo Wang, Keyuan Cheng, Muhammad Asif Ali, Yunlong Lu, Wenxin Li

TL;DR

This survey addresses the fragmentation of AI-generated game commentary (AI-GGC) by proposing a unified framework organized around three core commentator capabilities—Live Observation, Strategic Thinking, and Historical Recall—and three commentary types: Descriptive, Analytical, and Background. It systematically reviews methods, datasets, and evaluation metrics across board games, sports, and esports, and outlines challenges in real-time reasoning, multimodal integration, and evaluation design. The authors introduce an integrated scheme for analyzing AI-GGC along game genres, capabilities, and commentary types, and provide a public resource repository to standardize evaluation and data practices. By delivering a coherent taxonomy and cross-genre synthesis, the paper aims to accelerate the development of cohesive, scalable AI-GGC systems with practical impact for broadcast, analytics, and viewer personalization.

Abstract

The advent of artificial intelligence has propelled AI-Generated Game Commentary (AI-GGC) into a rapidly expanding field, offering benefits such as unlimited availability and personalized narration. However, current researches in this area remain fragmented, and a comprehensive survey that systematically unifies existing efforts is still missing. To bridge this gap, our survey introduces a unified framework that systematically organizes the AI-GGC landscape. We present a novel taxonomy focused on three core commentator capabilities: Live Observation, Strategic Analysis, and Historical Recall. Commentary is further categorized into three functional types: Descriptive, Analytical, and Background. Building on this structure, we provide an in-depth review of state-of-the-art methods, datasets, and evaluation metrics across various game genres. Finally, we highlight key challenges such as real-time reasoning, multimodal integration, and evaluation bottlenecks, and outline promising directions for future research and system development in AI-GGC.

From Multimodal Perception to Strategic Reasoning: A Survey on AI-Generated Game Commentary

TL;DR

This survey addresses the fragmentation of AI-generated game commentary (AI-GGC) by proposing a unified framework organized around three core commentator capabilities—Live Observation, Strategic Thinking, and Historical Recall—and three commentary types: Descriptive, Analytical, and Background. It systematically reviews methods, datasets, and evaluation metrics across board games, sports, and esports, and outlines challenges in real-time reasoning, multimodal integration, and evaluation design. The authors introduce an integrated scheme for analyzing AI-GGC along game genres, capabilities, and commentary types, and provide a public resource repository to standardize evaluation and data practices. By delivering a coherent taxonomy and cross-genre synthesis, the paper aims to accelerate the development of cohesive, scalable AI-GGC systems with practical impact for broadcast, analytics, and viewer personalization.

Abstract

The advent of artificial intelligence has propelled AI-Generated Game Commentary (AI-GGC) into a rapidly expanding field, offering benefits such as unlimited availability and personalized narration. However, current researches in this area remain fragmented, and a comprehensive survey that systematically unifies existing efforts is still missing. To bridge this gap, our survey introduces a unified framework that systematically organizes the AI-GGC landscape. We present a novel taxonomy focused on three core commentator capabilities: Live Observation, Strategic Analysis, and Historical Recall. Commentary is further categorized into three functional types: Descriptive, Analytical, and Background. Building on this structure, we provide an in-depth review of state-of-the-art methods, datasets, and evaluation metrics across various game genres. Finally, we highlight key challenges such as real-time reasoning, multimodal integration, and evaluation bottlenecks, and outline promising directions for future research and system development in AI-GGC.

Paper Structure

This paper contains 23 sections, 3 figures.

Table of Contents

  1. Introduction
  2. AI-GGC Survey Scheme
  3. Scope and Game Genres
  4. Core Capabilities of AI Commentators
  5. Taxonomy of Commentary Types
  6. Methods
  7. Live Observation
  8. Strategic Analysis
  9. Historical Recall
  10. Content Selection
  11. Commentary Generation and Rendering
  12. Datasets and Benchmarks
  13. Dataset Composition and Modalities
  14. Board Game Commentary Datasets
  15. Sports Commentary Datasets
  16. ...and 8 more sections

Figures (3)

  • Figure 1: The Three Common Genres of Game Commentary: Sports, Board Games, and E-Sports.
  • Figure 2: Taxonomy of Methods, Datasets and Metrics in AI-GGC
  • Figure 3: Overview of the proposed AI-GGC survey scheme, systematically summarizing the field along three key dimensions: (a) Game genres; (b) Foundational capabilities of AI commentators; (c) Commentary types.