提出评估生成引擎的双阶段框架，揭示引用广度与深度的差异。
From Citation Selection to Citation Absorption: A Measurement Framework for Generative Engine Optimization Across AI Search Platforms
- 分两阶段测量引用选择与引用吸收，识别生成质量关键环节。
- ChatGPT引用少但影响力高，长文结构化内容更易被吸收。
- 适合关注生成式搜索优化与信息吸收效果的研究者参考。
生成式搜索引擎决定信息是仅可发现、被引用，还是真正融入生成答案。本文提出生成引擎优化（GEO）的双阶段测量框架：引用选择（平台触发搜索并选源）与引用吸收（被引页面贡献语言、证据、结构或事实支持）。分析覆盖602个受控提示的geo-citation-lab数据集，包含ChatGPT、Google AI Overview/Gemini和Perplexity；21,143条有效搜索层引用；23,745条引用级特征记录；18,151条成功获取页面；72个提取特征。核心发现为引用广度与深度分化：Perplexity和Google平均引用更多来源，而ChatGPT引用较少但被获取页面的平均影响力显著更高。高影响力页面通常更长、结构更清晰、语义更契合，并富含定义、数值事实、比较和步骤等可提取证据。结果表明，GEO应超越引用数量衡量，将答案级吸收视为独立结果。
原文摘要 · Abstract (English)
Generative search engines increasingly determine whether online information is merely discoverable, cited as a source, or actually absorbed into generated answers. This paper proposes a two-stage measurement framework for Generative Engine Optimization (GEO): citation selection, where a platform triggers search and chooses sources, and citation absorption, where a cited page contributes language, evidence, structure, or factual support to the final answer. We analyze the public geo-citation-lab dataset covering 602 controlled prompts across ChatGPT, Google AI Overview/Gemini, and Perplexity; 21,143 valid search-layer citations; 23,745 citation-level feature records; 18,151 successfully fetched pages; and 72 extracted features. The central descriptive finding is that citation breadth and citation depth diverge. Perplexity and Google cite more sources on average, while ChatGPT cites fewer sources but shows substantially higher average citation influence among fetched pages. High-influence pages tend to be longer, more structured, semantically aligned, and richer in extractable evidence such as definitions, numerical facts, comparisons, and procedural steps. The results suggest that GEO should be measured beyond citation counts, with answer-level absorption treated as a separate outcome.
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