TableTale: 重唤科学论文中数据表格与文本之间的叙述互动
TableTale: Reviving the Narrative Interplay Between Data Tables and Text in Scientific Papers
AI总结：
TableTale通过增强阅读界面，利用多粒度对齐和链接机制，提升科学论文中文本与表格的叙述互动，降低认知负荷并提高阅读效率。
摘要：
数据表格在科学论文中起着核心作用。然而，其意义往往通过叙述互动与周围文本共同构建，使读者的理解在认知上具有挑战性。在本工作中，我们探讨了接口如何更好地支持这一阅读过程。我们进行了一项形成性研究，揭示了文本-表格叙述互动的关键特征，包括链接机制、多粒度对齐和提及类型学，以及读者意图的分层框架。基于这些见解，我们提出了TableTale，一种增强的阅读界面，它在多个粒度上丰富文本与数据表格，包括段落、句子和提及。TableTale在论文层面自动构建链接方案，并逐步在文本和表格上呈现渐进的视觉提示，这些提示随着读者在文本中的移动而展开。一项包含24名参与者的被试内研究显示，TableTale减少了认知负荷并提高了阅读效率，证明了其在增强论文阅读和指导未来阅读界面设计方面的潜力。
英文摘要：
Data tables play a central role in scientific papers. However, their meaning is often co-constructed with surrounding text through narrative interplay, making comprehension cognitively demanding for readers. In this work, we explore how interfaces can better support this reading process. We conducted a formative study that revealed key characteristics of text-table narrative interplay, including linking mechanisms, multi-granularity alignments, and mention typologies, as well as a layered framework of readers' intents. Informed by these insights, we present TableTale, an augmented reading interface that enriches text with data tables at multiple granularities, including paragraphs, sentences, and mentions. TableTale automatically constructs a document-level linking schema within the paper and progressively renders cascade visual cues on text and tables that unfold as readers move through the text. A within-subject study with 24 participants showed that TableTale reduced cognitive workload and improved reading efficiency, demonstrating its potential to enhance paper reading and inform future reading interface design.
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。
