连接点：通过AI生成的跨模态链接揭示文档结构
Connecting the Dots: Surfacing Structure in Documents through AI-Generated Cross-Modal Links
AI总结：
本文提出通过AI生成跨模态链接的框架，帮助用户更高效地理解和整合复杂文档中的信息。
摘要：
理解信息密集的文档（如食谱和科学论文）需要读者找到、解释并连接文本、图表、表格和其他视觉元素中的细节。这些文档通常较长，充满专业术语，阻碍了查找相关信息或拼凑相关想法的能力。现有工具在跨媒体类型整合信息方面支持有限。因此，理解复杂材料仍具有认知负担。本文提出了一种复杂文档中细粒度信息整合的框架。我们将其实例化为一个增强阅读界面，该界面在科学论文中添加可点击的图表点、正文中的交互式高亮和一个持久的参考面板，用于访问汇总细节而无需手动滚动。在受控的组间研究中，我们发现使用我们工具的参与者在阅读测验中得分显著更高，没有证据显示完成时间增加或认知负荷增加。细粒度整合提供了一种系统揭示文档内部关系的方法，支持与复杂、信息密集材料的互动。
英文摘要：
Understanding information-dense documents like recipes and scientific papers requires readers to find, interpret, and connect details scattered across text, figures, tables, and other visual elements. These documents are often long and filled with specialized terminology, hindering the ability to locate relevant information or piece together related ideas. Existing tools offer limited support for synthesizing information across media types. As a result, understanding complex material remains cognitively demanding. This paper presents a framework for fine-grained integration of information in complex documents. We instantiate the framework in an augmented reading interface, which populates a scientific paper with clickable points on figures, interactive highlights in the body text, and a persistent reference panel for accessing consolidated details without manual scrolling. In a controlled between-subjects study, we find that participants who read the paper with our tool achieved significantly higher scores on a reading quiz without evidence of increased time to completion or cognitive load. Fine-grained integration provides a systematic way of revealing relationships within a document, supporting engagement with complex, information-dense materials.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
