用树状结构+大模型摘要，让论文阅读更高效
TreeReader: A Hierarchical Academic Paper Reader Powered by Language Models
- 将论文拆成可交互的树形结构，每部分用大模型生成摘要
- 用户研究显示阅读效率提升，关键信息定位更快
- 适合需要快速掌握文献核心的研究者和学生
高效阅读学术论文对科学进步至关重要。传统线性格式（如PDF、HTML）易造成认知负荷，掩盖论文的层级结构，难以定位关键信息。尽管基于大语言模型的聊天机器人可提供摘要，但往往缺乏对特定章节的细致理解，可能生成不可靠信息，且通常忽略文档的导航结构。通过一项关于学术阅读行为的形成性研究，我们提出TreeReader——一种由语言模型增强的新型论文阅读工具。TreeReader将论文分解为交互式树形结构，每个章节初始以大模型生成的简洁摘要呈现，细节内容可按需展开。该设计使用户能快速把握核心思想，选择性深入感兴趣部分，并对照原文验证摘要内容。用户研究表明，TreeReader通过融合层次化摘要与交互式探索，显著提升了阅读效率与理解深度。
原文摘要 · Abstract (English)
Efficiently navigating and understanding academic papers is crucial for scientific progress. Traditional linear formats like PDF and HTML can cause cognitive overload and obscure a paper's hierarchical structure, making it difficult to locate key information. While LLM-based chatbots offer summarization, they often lack nuanced understanding of specific sections, may produce unreliable information, and typically discard the document's navigational structure. Drawing insights from a formative study on academic reading practices, we introduce TreeReader, a novel language model-augmented paper reader. TreeReader decomposes papers into an interactive tree structure where each section is initially represented by an LLM-generated concise summary, with underlying details accessible on demand. This design allows users to quickly grasp core ideas, selectively explore sections of interest, and verify summaries against the source text. A user study was conducted to evaluate TreeReader's impact on reading efficiency and comprehension. TreeReader provides a more focused and efficient way to navigate and understand complex academic literature by bridging hierarchical summarization with interactive exploration.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。