TreeReader: A Hierarchical Academic Paper Reader Powered by Language Models
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.
Cite
@article{arxiv.2507.18945,
title = {TreeReader: A Hierarchical Academic Paper Reader Powered by Language Models},
author = {Zijian Zhang and Pan Chen and Fangshi Du and Runlong Ye and Oliver Huang and Michael Liut and Alán Aspuru-Guzik},
journal= {arXiv preprint arXiv:2507.18945},
year = {2025}
}