MoDora: Tree-Based Semi-Structured Document Analysis System
Summary: arXiv:2602.23061v3 Announce Type: replace-cross
Abstract: Semi-structured documents integrate diverse interleaved data elements (e.g., tables, charts, hierarchical paragraphs) arranged in various and often irregular layouts. These documents are widely observed across domains and account for a large portion of real-world data. However, existing methods struggle to support natural language question answering over these documents due to three main technical challenges:
- The elements extracted by techniques like OCR are often fragmented and stripped of their original semantic context, making them inadequate for analysis.
- Existing approaches lack effective representations to capture hierarchical structures within documents (e.g., associating tables with nested chapter titles) and to preserve layout-specific distinctions (e.g., differentiating sidebars from main content).
- Answering questions often requires retrieving and aligning relevant information scattered across multiple regions or pages, such as linking a descriptive paragraph to table cells located elsewhere in the document.
To address these issues, we propose MoDora, an LLM-powered system for semi-structured document analysis. The system is designed to enhance the process of extracting and analyzing information from complex documents through several innovative strategies:
- Local-Alignment Aggregation Strategy: This strategy converts OCR-parsed elements into layout-aware components and conducts type-specific information extraction for components with hierarchical titles or non-text elements.
- Component-Correlation Tree (CCTree): MoDora utilizes a CCTree to hierarchically organize components. This model explicitly captures inter-component relations and layout distinctions through a bottom-up cascade summarization process.
- Question-Type-Aware Retrieval Strategy: The system supports layout-based grid partitioning for location-based retrieval and LLM-guided pruning for semantic-based retrieval, enabling it to effectively answer complex queries.
Experiments conducted demonstrate that MoDora significantly outperforms existing baseline methods by achieving accuracy improvements ranging from 5.97% to 61.07%. This remarkable performance highlights the potential of MoDora in transforming how we analyze semi-structured documents and respond to natural language inquiries.
For developers and researchers interested in exploring MoDora further, the source code is available at https://github.com/weAIDB/MoDora. This open-source availability allows for community contributions and further enhancements to the system.
As the demand for sophisticated document analysis tools continues to rise, particularly in fields like data science, legal analysis, and academic research, MoDora stands out as a promising solution. Its innovative approach to handling the complexities of semi-structured documents could pave the way for more efficient data extraction and analysis methodologies.