发表机构
Brigham Young University（杨百翰大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究通过访谈读者，探讨了读者对AI生成脚注的偏好，并提出了评估脚注质量的主题框架，涵盖信息来源、内容和呈现方式。
AI 中文摘要
脚注可以成为帮助理解的强大工具，提供增强阅读体验的信息。然而，静态脚注无法解决每位读者的疑问。当前的阅读工具允许读者查看精选脚注，支持个人和社交注释，并将词典链接到阅读材料。许多其他现有工具和自然语言处理（NLP）技术——如生成式AI、摘要和翻译——可用于解决任何读者疑问。然而，尚未有人探索读者实际需要这些功能中的哪些。为弥合这一差距，我们对来自不同背景的读者进行了十三次半结构化访谈，随后对其回答进行了主题分析。我们开发了描述读者偏好的脚注类型以及如何确定脚注质量的主题——特别关注系统考虑哪些信息来源、脚注包含什么内容，以及脚注如何呈现给读者。
英文摘要
Footnotes can be powerful tools to aid understanding, providing information that augments the reading experience. However, static footnotes cannot address every reader question. Current reading tools allow readers to view curated footnotes, allow personal and social annotation, and link dictionaries to reading material. Many other existing tools and natural language processing (NLP) techniques--such as generative AI, summarization and translation--could be used to address any reader question. However, no one has yet explored which of these features readers actually want. To bridge this gap, we conducted thirteen semi-structured interviews with readers from various backgrounds, followed by a thematic analysis of their responses. We develop themes describing the types of footnotes readers prefer and how to determine the quality of footnotes--specifically focusing on what sources of information a system considers, what the footnotes contain, and how the footnotes are presented to the reader.
