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.

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