InsightToast：数据密集型会议侧信道中的主动信息检索与概览式可视化
InsightToast: Proactive Information Retrieval & Glanceable Visualization in the Side Channel of Data-Rich Meetings
AI总结：
该研究提出InsightToast应用，可实时监控会议对话并主动检索信息生成见解，16人对比研究显示其能帮助参与者在维持对话的同时做出明智政策决策。
AI中文摘要：
会议中缺失的制度背景会阻碍有效参与。检索相关信息（通常分散在异构的内部和外部来源）需要代价高昂的任务切换，这会破坏个人注意力和集体对话流程，在决策等认知要求高的任务中尤其有害。我们推出InsightToast，这是一款混合主动应用，可实时监控口头对话，识别出现的主题和信息需求，并通过基于多智能体大语言模型（LLM）的流水线主动检索相关信息，该流水线整合了检索增强生成（RAG），以生成基于来源的简洁文本见解和概览式交互式图表，通过外围界面作为对话侧信道中的短暂提示（toasts）呈现。为展示其产生偶然见解的潜力，我们展示了一个涉及立法文件知识库作为会议背景的使用场景。随后我们报告了一项对比研究（N=16），其中参与者在保持自然对话流程的同时做出了明智的政策决策。
英文摘要：
Missing institutional context during meetings can impede effective participation. Retrieving relevant information, often scattered across heterogeneous internal and external sources, requires costly task-switching that disrupts both individual focus and collective conversational flow, particularly detrimental during cognitively demanding tasks such as decision-making. We introduce InsightToast, a mixed-initiative application that monitors verbal discourse in real time, identifies topics and informational needs as they emerge, and proactively retrieves relevant information through a multi-agent large language model (LLM)-based pipeline integrating retrieval-augmented generation (RAG) to produce source-grounded insights as succinct text and glanceable interactive charts, delivered through a peripheral interface as ephemeral toasts in the conversation's side channel. To demonstrate the potential for yielding serendipitous insights, we showcase a usage scenario involving a knowledge base of legislative documents as the meeting's context. We then report on a comparative study (N=16), in which participants arrived at informed policy decisions while maintaining natural conversation flow.
