TreeReader: A Hierarchical Academic Paper Reader Powered by Language Models

• URL: http://arxiv.org/abs/2507.18945v1
• Date: Fri, 25 Jul 2025 04:31:09 GMT
• Title: TreeReader: A Hierarchical Academic Paper Reader Powered by Language Models
• Authors: Zijian Zhang, Pan Chen, Fangshi Du, Runlong Ye, Oliver Huang, Michael Liut, Alán Aspuru-Guzik,
• Abstract summary: TreeReader is a novel language model-augmented paper reader.<n>It decomposes papers into an interactive tree structure where each section is represented by a concise summary.<n>TreeReader provides a more focused and efficient way to navigate and understand complex academic literature.
• Score: 4.051630462399835
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: 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.

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