TreeReader: A Hierarchical Academic Paper Reader Powered by Language Models

2025-07-25

AI Summary

Traditional academic papers’ linear presentation induces cognitive overload, obscures hierarchical structure, and impedes rapid identification of key information; existing LLM-based summarization tools lack fine-grained semantic understanding and structural fidelity, compromising credibility and verifiability. This paper proposes a hierarchical interactive paper reading system that parses document structure and generates paragraph-level LLM summaries to construct an expandable, navigable tree-structured interface, enabling on-demand browsing and traceable verification. The system fully preserves the original logical organization, realizing a closed-loop reading paradigm: “overview → focus → backtrack.” User studies demonstrate significant improvements in information retrieval speed (+42%) and conceptual comprehension accuracy (+31%), establishing a novel paradigm for efficient, deep reading of scientific literature.

Technology Category

• Natural Language Processing: Summarization
• Data Mining & Knowledge Management: Data Visualization & Summarization
• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior

Application Category

• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation

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.

Problem

• Traditional linear formats obscure hierarchical paper structure
• LLM chatbots lack nuanced section understanding and reliability
• Current methods discard document navigational structure

Innovation

• Hierarchical tree structure for paper navigation
• LLM-generated summaries for each section
• Interactive exploration with on-demand details