DocRefine: An Intelligent Framework for Scientific Document Understanding and Content Optimization based on Multimodal Large Model Agents

TL;DR

DocRefine is an advanced multimodal framework that uses a multi-agent system with large models to understand, edit, and summarize scientific PDFs with high accuracy and fidelity.

Contribution

It introduces a multi-agent system leveraging LVLMs for precise scientific document understanding and content optimization, outperforming existing methods.

Findings

Achieves high semantic consistency and layout fidelity scores.

Outperforms state-of-the-art baselines on the DocEditBench dataset.

Demonstrates effective handling of complex multimodal document editing.

Abstract

The exponential growth of scientific literature in PDF format necessitates advanced tools for efficient and accurate document understanding, summarization, and content optimization. Traditional methods fall short in handling complex layouts and multimodal content, while direct application of Large Language Models (LLMs) and Vision-Language Large Models (LVLMs) lacks precision and control for intricate editing tasks. This paper introduces DocRefine, an innovative framework designed for intelligent understanding, content refinement, and automated summarization of scientific PDF documents, driven by natural language instructions. DocRefine leverages the power of advanced LVLMs (e.g., GPT-4o) by orchestrating a sophisticated multi-agent system comprising six specialized and collaborative agents: Layout & Structure Analysis, Multimodal Content Understanding, Instruction Decomposition, Content Refinement, Summarization & Generation, and Fidelity & Consistency Verification. This closed-loop feedback architecture ensures high semantic accuracy and visual fidelity. Evaluated on the comprehensive DocEditBench dataset, DocRefine consistently outperforms state-of-the-art baselines across various tasks, achieving overall scores of 86.7% for Semantic Consistency Score (SCS), 93.9% for Layout Fidelity Index (LFI), and 85.0% for Instruction Adherence Rate (IAR). These results demonstrate DocRefine's superior capability in handling complex multimodal document editing, preserving semantic integrity, and maintaining visual consistency, marking a significant advancement in automated scientific document processing.