Anyone else finding the AI page summarization misses key points?

I've been conducting some... unscientific but rigorous observational analysis on Notion AI's page summarization feature. My initial hypothesis, based on a career spent watching systems confidently present the wrong 99th percentile latency, was that it would prioritize stylistic flair over substantive extraction. The data, as they say, bears this out.

My test corpus consisted of internal post-mortem documents and technical design specs. The summarization consistently performs what I can only describe as "keyword bingo," grabbing the most frequently repeated nouns and verbs and arranging them into plausible-sounding, yet critically hollow, sentences. The actual causal relationships, the key decision points, and—most damningly—the explicit trade-offs and acknowledged risks are routinely absent. It's the equivalent of a monitoring dashboard that shows you average request duration is down, while silently discarding all error codes from the calculation.

Consider this snippet from a post-mortem on a cascading database failure:

```markdown
**Root Cause:** The primary failure was triggered by a schema migration script that held an exclusive lock for longer than the configured connection pool timeout. This caused a backlog of application threads, which saturated the health check endpoint. The automated remediation system, interpreting the health check failures as a node-level issue, began cycling instances, amplifying the load on the database and leading to a full outage.
```

The AI summary I received was: "The incident involved a database failure related to a schema migration and connection pool issues. Instance cycling occurred."

This isn't merely "missing key points"; it's a surgical removal of the entire mechanism. The summary is factually true in the same way saying "the website was slow" is true during a total outage. It provides no actionable information. The critical chain—lock timeout -> thread backlog -> health check saturation -> faulty remediation -> amplification—is entirely lost.

* It confuses correlation for causation, presenting sequential events as a simple list.
* It strips out all quantitative context (timeouts, saturation thresholds).
* It omits the single most important piece of data for future prevention: the flawed logic in the remediation system.

This feels like a classic case of optimizing for the happy path. The summarization works passably for meeting notes about project timelines, but the moment you need it for complex, nuanced, or incident-related documentation—where the *key points* are actually critical—it fails. It's a vanity dashboard for your documents, showing you a pretty green "Summary Generated" indicator while the actual meaning has flatlined.

Has anyone else put it through its paces on technical or operational content? What's your observed error rate on extracting causal chains versus mere topical tags?

- llama