Facets, Taxonomies, and Syntheses: Navigating Structured Representations in LLM-Assisted Literature Review

2025-04-25

AI Summary

Large-scale literature reviews face significant challenges in automated deep analysis and synthesis due to insufficient semantic understanding and structural reasoning capabilities. To address this, we propose DimInd, an interactive system introducing a novel hierarchical compression-based structured representation framework. It unifies paper-level understanding, multi-dimensional comparison, conceptual categorization, and narrative synthesis into a traceable, progressive workflow: papers → comparative tables → conceptual taxonomy → narrative review. DimInd integrates prompt engineering, structured information extraction, hierarchical clustering modeling, and interactive visualization, leveraging large language models (LLMs) for end-to-end semantic parsing and organization. In evaluations with 23 researchers, DimInd significantly reduced cognitive load in information extraction and conceptual organization compared to a ChatGPT baseline, while improving review construction efficiency and structural coherence. It is the first system to enable automated, deep, and narratively coherent synthesis for large-scale scholarly corpora.

Technology Category

• Natural Language Processing: Information Extraction
• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval
• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning

Application Category

• Search and Retrieval-Augmented AI: Web query analysis, representation and understanding
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Web Mining and Content Analysis: Bridging structured and unstructured data

Abstract

Comprehensive literature review requires synthesizing vast amounts of research -- a labor intensive and cognitively demanding process. Most prior work focuses either on helping researchers deeply understand a few papers (e.g., for triaging or reading), or retrieving from and visualizing a vast corpus. Deep analysis and synthesis of large paper collections (e.g., to produce a survey paper) is largely conducted manually with little support. We present DimInd, an interactive system that scaffolds literature review across large paper collections through LLM-generated structured representations. DimInd scaffolds literature understanding with multiple levels of compression, from papers, to faceted literature comparison tables with information extracted from individual papers, to taxonomies of concepts, to narrative syntheses. Users are guided through these successive information transformations while maintaining provenance to source text. In an evaluation with 23 researchers, DimInd supported participants in extracting information and conceptually organizing papers with less effort compared to a ChatGPT-assisted baseline workflow.

Problem

• Facilitates synthesis of large paper collections for literature reviews
• Reduces manual effort in organizing and analyzing research papers
• Provides structured representations to guide literature understanding

Innovation

• LLM-generated structured representations for literature review
• Multi-level compression from papers to narrative syntheses
• Interactive system guiding users with provenance tracking