一种以指示语为中心的数据可视化远程同步交流记录方法
A Deixis-Centered Approach for Documenting Remote Synchronous Communication around Data Visualizations
AI总结：
本文提出一种以指示语为中心的协作数据会议记录方法，通过捕捉光标手势与音频并利用大语言模型生成带上下文注释的交互式文档，用户研究表明其优于录像、文字记录和手动笔记。
摘要：
指称性手势，在语言学中称为指示语（deixis），是围绕数据可视化进行交流的重要组成部分。尽管它们很重要，但在记录数据分析会议时，这类手势往往被忽视。例如，文字记录无法捕捉手势，而视频录像可能无法充分捕捉或突出它们。我们提出了一种新颖的协作数据会议记录方法，将指示语视为一等公民。我们提出的框架捕捉基于光标的手势数据以及音频，并将其转换为交互式文档。该框架利用大型语言模型来识别词语与手势之间的对应关系。这些识别出的指称被用于在生成的交互式文档中创建基于上下文的注释。我们通过一项用户研究评估了所提方法的有效性，发现参与者更喜欢我们的自动化交互式文档，而不是录像、文字记录和手动记笔记。此外，我们从研究期间参与者的行为中推导出了一个基于光标的指示性手势的初步分类法。该分类法为在协作数据分析场景中更好地利用基于光标的指示语提供了更多机会。
英文摘要：
Referential gestures, or as termed in linguistics, deixis, are an essential part of communication around data visualizations. Despite their importance, such gestures are often overlooked when documenting data analysis meetings. Transcripts, for instance, fail to capture gestures, and video recordings may not adequately capture or emphasize them. We introduce a novel method for documenting collaborative data meetings that treats deixis as a first-class citizen. Our proposed framework captures cursor-based gestural data along with audio and converts them into interactive documents. The framework leverages a large language model to identify word correspondences with gestures. These identified references are used to create context-based annotations in the resulting interactive document. We assess the effectiveness of our proposed method through a user study, finding that participants preferred our automated interactive documentation over recordings, transcripts, and manual note-taking. Furthermore, we derive a preliminary taxonomy of cursor-based deictic gestures from participant actions during the study. This taxonomy offers further opportunities for better utilizing cursor-based deixis in collaborative data analysis scenarios.
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