Navigating the vast scientific literature often starts with browsing a paper's abstract. However, when a reader seeks additional information, not present in the abstract, they face a costly cognitive chasm during their dive into the full text. To bridge this gap, we introduce recursively expandable abstracts, a novel interaction paradigm that dynamically expands abstracts by progressively incorporating additional information from the papers' full text. This lightweight interaction allows scholars to specify their information needs by quickly brushing over the abstract or selecting AI-suggested expandable entities. Relevant information is synthesized using a retrieval-augmented generation approach, presented as a fluid, threaded expansion of the abstract, and made efficiently verifiable via attribution to relevant source-passages in the paper. Through a series of user studies, we demonstrate the utility of recursively expandable abstracts and identify future opportunities to support low-effort and just-in-time exploration of long-form information contexts through LLM-powered interactions.

翻译：导航海量科学文献通常从浏览论文摘要开始。然而，当读者需要获取摘要中未包含的补充信息时，在深入研读全文过程中会面临认知断层的高昂代价。为弥合这一鸿沟，我们提出可递归扩展摘要——一种动态扩展摘要的新型交互范式，通过渐进式整合论文全文中的补充信息实现智能延展。这种轻量级交互允许学者通过快速浏览摘要或选择AI建议的可扩展实体来指定信息需求。相关信息通过检索增强生成方法进行合成，以流畅的线程式摘要扩展形式呈现，并通过归因至论文中的相关源段落实现高效可验证性。通过系列用户研究，我们验证了可递归扩展摘要的实用性，并识别了通过大语言模型驱动交互支持低投入、即时探索长文本信息场景的未来机遇。