• Milan E. Ismagulov, Yugra state university (Khanty-Mansiysk, Russia)

This article proposes a document structure description model based on Hierarchical Document Structure Analysis (HDSA) methods and the formalism of graph theory. Within the proposed approach, a document is considered as a hierarchical system of interconnected structural elements, including headings of different levels, text blocks, tables, formulas, images, and other types of content. Each element is interpreted as a graph vertex, while nesting and logical dependency relationships between elements are described using directed edges of the graph structure.
The application of the proposed model to a corpus of documents from various subject areas made it possible to identify stable patterns in the organization of textual material characteristic of specific document types. Based on the obtained characteristics, a set of JSON templates was developed to describe typical document structures for different subject areas, including technical, legal, and scientific and educational materials.
Each template contains a formalized description of the document structure, including permissible heading levels, constraints on content types, and parameters of stylistic and compositional formatting. Additionally, a template may include information on the preferred data presentation format, rules for generating tables and formulas, and specialized textual constructions. The use of the JSON format provides a machine-oriented representation of the document structure and enables the integration of templates into software systems for automatic text generation. The developed templates are intended for use in text generation by large language models (LLMs). Within the proposed approach, a template serves as a formalized structural constraint that defines the permissible organization of the resulting document and the sequence in which its sections are generated. This makes it possible to reduce the structural variability of generated text, improve the consistency of interrelated sections, and ensure that the resulting document complies with the requirements of a specific subject area.
The proposed approach is aimed at implementing controlled document generation, in which a large language model generates content within a predefined structural scheme. This provides more stable document generation quality, reduces the likelihood of violations in the logical organization of the text, and improves the reproducibility of generation results. Furthermore, the use of structural templates makes it possible to adapt the generation process to different document types without modifying the architecture of the language model itself.

hierarchical document structure, graph theory, JSON templates, large language models (LLMs), structured text generation, structural patterns, document representation model

2026-09-03