Evaluating AI Meeting Summaries with a Reusable Cross-Domain Pipeline
Industrial teams often deploy large language model features before stable regression or model selection evaluation exists. We present a reusable evaluation system for AI meeting summaries that combines structured ground-truth (GT) construction, fixed candidate generation, claim-grounded scoring, persisted reporting, and a privacy-bounded online monitoring and nomination interface. The online evidence is not itself a benchmark: privacy-safe aggregate exports show active monitoring, hard regime detection, and directional movement without exposing customer data. We benchmark the offline path on 114 meetings across city_council, private_data, and whitehouse_press_briefings, yielding 340 compl...
| Authors | Philip Zhong, Don Wang, Jason Zhang |
|---|---|
| Primary category | cs.AI |
| Published | 23 April 2026 |
| Last updated at source | 13 May 2026 |
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