TreeReader: A Hierarchical Academic Paper Reader Powered by Language Models

TL;DR

TreeReader is an interactive hierarchical paper reader powered by language models, designed to improve navigation, comprehension, and efficiency in understanding complex academic papers by organizing content into an accessible tree structure.

Contribution

It introduces a novel hierarchical paper reading interface that combines LLM-generated summaries with interactive exploration, addressing limitations of traditional formats and chatbots.

Findings

Enhanced reading efficiency and comprehension in user study

Effective navigation through hierarchical summaries and details

Improved focus on key paper sections

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.