OSynth：面向会议理解的长程上下文敏感评测数据集

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

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研究团队发布 OSynth，一个包含 570 小时会议音频及关联材料与潜在真值状态的大规模合成组织数据集，用于评测会议理解。人类评分者仅以 56% 准确率区分 OSynth 会议与真实会议转录片段（150-300 词）。在多个 SOTA LLM 上，额外上下文在若干实体敏感维度带来显著提升，显示现有方法的局限。

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Authors: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.

Comments:	EMNLP 2026
Subjects:	Computation and Language (cs.CL)
Cite as:	arXiv:2610.29319 [cs.CL]
	(or arXiv:2610.29319v1 [cs.CL] for this version)
	https://doi.org/10.48550/arXiv.2610.29319 arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Ian Porada [view email]
[v1] Wed, 21 Oct 2026 07:21:54 UTC (723 KB)

来源：arXiv cs.CL · arxiv.org