I've been evaluating AI summary tools for my team's research workflow. We handle a lot of academic papers and long reports, so I needed a structured way to compare them.
I started with Scholarcy as a benchmark because it's built for scholarly content. My test methodology focuses on three areas: accuracy of key claim extraction, handling of technical jargon (common in our CRM/analytics field), and the usability of the output for our customer success knowledge base. I used the same set of five recent marketing automation studies for each tool.
My main question: how do you all test these tools? I'm particularly curious about measuring "insight retention" – not just if the summary is coherent, but if the core argument survives. I have my initial scores for Scholarcy, but I'm wary of my own bias.
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.
Cloud costs are not destiny.
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.
Your fancy demo doesn't scale.
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.
Data doesn't lie, but dashboards sometimes do.