标题： 钻石：一种基于LLM的上下文感知棒球精彩集锦摘要代理 显示英文标题

标题： DIAMOND: An LLM-Driven Agent for Context-Aware Baseball Highlight Summarization

摘要： 传统方法——例如基于胜率增加值（WPA）的排名或计算机视觉驱动的事件检测——可以识别得分片段，但常常无法捕捉到战术深度、势头转换以及故事线的发展。人工整理仍然是黄金标准，但它资源密集且不可扩展。 我们引入了DIAMOND，这是一个由大型语言模型（LLM）驱动的上下文感知棒球高光摘要代理，它将结构化的体育分析与自然语言推理相结合。DIAMOND利用赛博计量学特征——胜率期望值、WPA和杠杆指数——来量化比赛的重要性，而LLM模块则通过上下文叙事价值增强选择。这种混合方法确保了定量严谨性和定性丰富性，超越了纯粹统计或基于视觉系统的局限性。 在五个不同的韩国职业棒球联盟比赛中进行评估时，DIAMOND的F1分数从WPA单独使用的42.9%提高到了84.8%，超过了商业和统计基线。尽管规模有限，我们的结果突显了模块化、可解释的基于代理框架在体育及其他领域事件级摘要中的潜力。 显示英文摘要

摘要： Traditional approaches -- such as Win Probability Added (WPA)-based ranking or computer vision-driven event detection -- can identify scoring plays but often miss strategic depth, momentum shifts, and storyline progression. Manual curation remains the gold standard but is resource-intensive and not scalable. We introduce DIAMOND, an LLM-driven agent for context-aware baseball highlight summarization that integrates structured sports analytics with natural language reasoning. DIAMOND leverages sabermetric features -- Win Expectancy, WPA, and Leverage Index -- to quantify play importance, while an LLM module enhances selection based on contextual narrative value. This hybrid approach ensures both quantitative rigor and qualitative richness, surpassing the limitations of purely statistical or vision-based systems. Evaluated on five diverse Korean Baseball Organization League games, DIAMOND improves F1-score from 42.9% (WPA-only) to 84.8%, outperforming both commercial and statistical baselines. Though limited in scale, our results highlight the potential of modular, interpretable agent-based frameworks for event-level summarization in sports and beyond.