首次系统研究在线会议摘要，提出高效策略与评估方法。
Policies and Evaluation for Online Meeting Summarization
- 设计多种在线摘要策略，支持实时生成
- 在AutoMin数据集上验证模型可生成高质量摘要
- 新指标可评估延迟与摘要质量的权衡，适合实际应用
随着会议日益数字化，会议摘要成为学术与产业界关注焦点。然而，现有研究多聚焦于会议结束后进行的离线摘要。本文首次系统研究在线会议摘要任务，提出多种摘要策略，分析其相较于离线设置的独特挑战，并定义新的评估指标以衡量延迟和部分摘要质量。在AutoMin数据集上的实验表明：1）在线模型能生成高质量摘要；2）所提指标可细致分析不同系统在质量-延迟之间的权衡，包括中间输出；3）自适应策略优于固定调度策略。这些发现为该重要任务的研究提供了起点。
原文摘要 · Abstract (English)
With more and more meetings moving to a digital domain, meeting summarization has recently gained interest in both academic and commercial research. However, prior academic research focuses on meeting summarization as an offline task, performed after the meeting concludes. In this paper, we perform the first systematic study of online meeting summarization. For this purpose, we propose several policies for conducting online summarization. We discuss the unique challenges of this task compared to the offline setting and define novel metrics to evaluate latency and partial summary quality. The experiments on the AutoMin dataset show that 1) online models can produce strong summaries, 2) our metrics allow a detailed analysis of different systems' quality-latency trade-off, also taking into account intermediate outputs and 3) adaptive policies perform better than fixed scheduled ones. These findings provide a starting point for the wider research community to explore this important task.
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