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arXiv · 2610.29319

OSynth: A Longitudinal, Context-Sensitive Evaluation of Meeting Understanding

Ian Porada·Suraj Tripathi·Chaitanya Animesh·Leda Sari·Chaitanya Narisetty·Austin R. Matthews·Bohan Yu·Ramesh L. Thulasiram·Gregory Sell·Kean Chin·Dmitriy Genzel
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Abstract

Meeting notetakers are increasingly deployed in settings where longitudinal context is available including calendar metadata, prior meetings, emails, slides, and speaker enrollments; however, meeting corpora remain limited in terms of size, domain coverage, and available context. We present OSynth, a large-scale synthetic dataset of simulated organizations comprising 570 hours of meeting audio together with associated artifacts and a latent ground-truth state. In a pairwise comparison, human raters distinguish OSynth meetings from real meeting transcripts with only 56% accuracy based on excerpts of 150-300 words. Across state-of-the-art LLMs, additional context yields significant gains on several entity-sensitive dimensions, demonstrating limitations of contemporary approaches and the potential usefulness of OSynth in developing better-contextualized meeting-understanding systems.

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Ian Porada, Suraj Tripathi, Chaitanya Animesh, Leda Sari, Chaitanya Narisetty, Austin R. Matthews, Bohan Yu, Ramesh L. Thulasiram, Gregory Sell, Kean Chin, Dmitriy Genzel. 2026-10-21. OSynth: A Longitudinal, Context-Sensitive Evaluation of Meeting Understanding. https://arxiv.org/abs/2610.29319

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