Showcase: My annotated methodology for testing AI summary tools (with Scholarcy data).

That "insight retention" question is the whole game. I've seen summaries that are fluent but completely invert a study's conclusion.

My team's cheap trick: pick one dense paragraph from your source material and run it through the tool. Then have a subject matter expert who *hasn't* read the full paper read the summary and tell you the main argument. See if it matches what you know is actually there.

For jargon handling, your CRM/analytics test is good. Watch for where the tool just passes jargon through verbatim versus where it tries to replace it with a generic term and loses meaning. The latter fails.

Self-bias is real. Are you scoring against an idealized summary you wrote yourself? That's a trap.

The SME test is a decent start, but it's not systematic. You're measuring the SME's interpretation, not the tool's output.

Take the source paragraph and the summary, anonymize both, and have the SME identify which claims are present in each. Then you get a percentage match. It's tedious, but it gives you a number instead of a feeling. For the jargon problem, you need a mapping of your specific terms to common synonyms beforehand, then check which ones the tool preserves.

Five papers is thin. You'll see wild variance between a methods-heavy section and a conclusion. Run at least twenty, and segment by paper section.

Thanks for sharing this, it's super helpful to see your test criteria! Your focus on "insight retention" is exactly what I'm wrestling with as I try to learn this stuff.

I'm curious about one thing: when you score the "usability of the output for our customer success knowledge base," how are you actually measuring that? Is someone on your team trying to use the summaries to answer real questions? That part seems tricky to test.

Measuring usability is tricky because it's about real work, not just a score. We had a simple rule: if the summary couldn't answer a "So what for us?" question in under 30 seconds, it failed.

For example, we'd give our customer success lead a summary of a paper on churn prediction and ask what action to test next month. If they got lost in generic statements, the tool wasn't useful. The jargon handling ties directly into this - if the summary strips out specific model names we use, it's not actionable.

Our takeaway was to treat usability as a pass/fail gate. If the team can't immediately use it, accuracy scores don't matter.

Your methodology's sound, but your sample size of five papers will kill you. The variance between a dense methods section and a breezy intro is huge. You need at least twenty, segmented by section type, to see real patterns. Scholarcy is a good benchmark, but it'll show its biases.

On bias: you're right to be wary. Don't score against your own idealized summary. You'll just measure how well the tool mimics your thinking. Use the anonymized claim-matching trick user810 mentioned. It's a hassle, but it gives you a hard percentage.

For insight retention, we used a cheap proxy: after generating a summary, we'd ask a simple "Therefore..." question. If the summary couldn't support a logical next step from the paper's core argument, the insight was lost. It's not perfect, but it's fast.

Your three-point methodology aligns with what we track for data pipeline summaries, where misinterpreting a schema change can break downstream dashboards. I've found "insight retention" directly correlates with a tool's ability to preserve logical connectors like "because," "therefore," or "however" from the source. Scholarcy often strips these out in favor of declarative statements, which flattens the argument.

I disagree that five papers is insufficient for an initial benchmark, provided you're stress-testing specific sections. For cost efficiency, we run each tool on the same complex methodological paragraph, the same results statement, and the same conclusion. If it fails on the methods, you know its limits for technical depth immediately.

Regarding your bias concern, don't write an idealized summary. Instead, extract the 3-5 core factual claims from the paper yourself as a control set. Then measure the summary's recall and precision against that set. It turns a subjective feeling into a metrics problem you can graph over your twenty-paper run later.

Totally agree about logical connectors being the secret sauce for insight retention. Scholarcy's flattening effect is exactly why we moved away from it for internal briefs.

Your control set idea is a game changer. We did something similar but called it a "claim inventory." It forced us to distinguish between core findings and supporting details before the tool even entered the picture. Made scoring way less fuzzy.

I'm with you on the five-paper benchmark. If a tool bombs on a dense methods section in a small batch, that's a valid, immediate red flag. You don't need twenty papers to spot a dealbreaker.

Moving away from a tool over something like logical connectors is a bit dramatic. If the claim inventory works, why switch? You're just trading one set of flaws for another.

The "immediate red flag" logic is fine for a prototype test. But committing to a tool based on five papers? That's how you end up with six months of cleanup when it chokes on a literature review. A dealbreaker today is just a known limitation tomorrow if you've only seen it fail once.

That "known limitation" logic can burn you when it's a core function, though. If a tool systematically strips out causality from methods sections, that's not a quirk - it means it can't process technical reasoning. We saw that with an early APM summary tool that turned "because the cache was cold" into "the response was slow." That's not a limitation you work around, it's a fundamental misrepresentation.

I do get the caution with small samples. But five well-chosen, dense paragraphs can absolutely reveal a pattern if the flaw is consistent across them. Sometimes you don't need a mountain of data to spot a broken foundation.