TreeReader: A Hierarchical Academic Paper Reader Powered by Language Models
This addresses the problem of inefficient academic reading for researchers and students, though it is incremental as it builds on existing LLM-based summarization with a novel interactive design.
The paper tackles the problem of cognitive overload and inefficient navigation in academic papers by introducing TreeReader, a hierarchical reader that decomposes papers into an interactive tree structure with LLM-generated summaries, resulting in improved reading efficiency and comprehension as shown in a user study.
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.