Summarization Loss: How Meaning Degrades Under Compression
AI-generated answers may quote, paraphrase, combine, or omit retrieved material. This SiteNexis framework identifies claims whose meaning depends on conditions that compression can remove.
AI-generated answers may quote, paraphrase, combine, or omit material from chunks retrieved from your page. Whenever a claim is compressed or separated from its surrounding context, qualifications can disappear. SiteNexis treats that vulnerability as summarization loss: a content-structure risk to inspect, not a prediction of how every provider will word an answer.
For example, “Feature X is available only on annual plans in supported regions” contains two conditions. A shortened answer that says “Feature X is available” changes the claim. Keeping the feature, plan condition, and regional condition in one self-contained sentence makes the dependency visible to both readers and extraction systems.
Claims That Survive Summarization
Within the SiteNexis model, lower-risk claims are precise, direct, and understandable without an adjacent paragraph. Specific numbers, dates, and names help only when their scope and source remain attached. A short sentence is not automatically safe if it drops the condition that makes the claim true.
Claims That Are Lost or Distorted
The model flags conditional, qualified, comparative, and cross-paragraph claims because their meaning depends on context that may not travel with the sentence. This is a structural warning, not evidence that a particular provider will rewrite the claim in a specific way.
▲Fragile claims — claims that require multi-chunk context to be accurately understood — are the highest-risk category for summarization distortion. If a key statistic only makes sense in the context of a methodology described in the previous paragraph, the summarized version of that statistic may be accurate in isolation but misleading without the methodological context.
Summarization Loss Score in SiteNexis
The Summarization Loss Score measures the proportion of claims on a page that are likely to survive summarization accurately. It identifies fragile claims — those with context dependencies or structural complexity that makes them prone to distortion — and reports a count of fragile claims per page. Pages with high fragile claim counts receive a lower Summarization Loss Score, which directly reduces the Retrieval Quality Score.
AI Visibility Engineering — Part 7 of 10