Hierarchical Section-Wise Writing

• Hierarchical Section-Wise Writing is a methodology that segments long documents into nested, logically organized sections using structured metadata and advanced algorithms.
• It employs TOC metadata, OCR-based title detection, and XML-derived features to accurately identify section boundaries, ensuring complex texts are effectively parsed.
• Hybrid approaches integrating LLMs refine section extraction by filtering noise and assigning hierarchy levels, optimizing document segmentation in legal and educational contexts.

Hierarchical section-wise writing refers to the systematic segmentation of long-form documents into nested, logically organized sections and subsections, often with explicit labels, boundaries, and cross-references. This paradigm is central to effective knowledge structuring, enabling improved readability, navigation, automated summarization, and downstream semantic analysis—especially in legal, technical, and educational contexts. Recent advances have focused on algorithmic segmentation of digital documents (notably PDFs of legal textbooks) into accurate hierarchical skeletons, leveraging both traditional Table of Contents (TOC) metadata and modern ML and LLM techniques. Research in this area offers comparative evaluations of TOC-driven, OCR-assisted, XML-feature-aware, and LLM-refined strategies for accurate extraction of section titles, determination of hierarchy depth, and robust section boundary identification (Wehnert et al., 31 Aug 2025).

1. Core Techniques for Hierarchical Segmentation

Hierarchical PDF segmentation in the context of complex, deeply nested texts necessitates robust detection of section titles and precise allocation of hierarchy levels. The primary techniques articulated in the literature include:

• TOC-Based PageParser: This method parses TOC metadata to obtain an ordered list of section titles and their hierarchy levels, maps extracted headings to the corresponding body text using exact, substring, or fuzzy matching, and then allocates section boundaries based upon these alignments. The procedure is implemented as a rule-based loop that sequentially processes pages, groups XML-derived text nodes into lines, and encodes results hierarchically ([Algorithm 1] in the source).
• LLM-Refined PageParser: Extends candidate detection by extracting potential headings from both XML and OCR outputs, then applies an LLM to semantically filter and refine these candidates. The LLM ensures both the correctness of title recognition and robust assignment of hierarchy levels, using patterns in detected headings for consistency.

These approaches are evaluated for their proficiency in handling three subtasks: title detection, level allocation, and section boundary assignment. Combining rule-based, structural, and semantic layers enables systems to handle noise, OCR artifacts, and inconsistencies in digital document formats.

2. Preprocessing and Feature Engineering

Preprocessing is pivotal for increasing segmentation robustness, especially for legal and historical texts:

• OCR-Based Title Detection: By applying tools such as Tesseract, systems identify headings based on spatial layout cues (e.g., lines surrounded by increased whitespace, multi-line headings) that may escape XML or typographic analysis.
• XML-Derived Features: These include font size, bold/italic attributes, spatial coordinates, and grouping—used for identifying typographic anomalies that typically signal section starts. However, XML parsing may miss purely spatial clues or misclassify multi-line headings.
• Integrative preprocessing combines these features, yielding candidate sets that are further filtered for redundancy and correctness, often by a subsequent LLM stage.

The combination increases fault tolerance and recall, particularly when one modality (e.g., XML) is incomplete or noisy.

3. Role of LLMs in Refinement

Integrating LLMs addresses semantic filtering and error reduction beyond rule-based pattern matching:

• Semantic Filtering: LLMs such as GPT-4, GPT-5, Llama3, or Phi4 remove unlikely titles (e.g., fragmented lines or misdetected numerals), validate section/head assignment, and help resolve ambiguous headings, especially in the presence of OCR or layout-induced noise.
• Hierarchy Allocation: LLMs provide context-aware inference for assignment of hierarchy levels, by reviewing content and detected headings both forward and backward through the document, ensuring that new chapters or sections are placed appropriately in the parent–child structure.
• Hybrid pipelines (e.g., XML-OCR-GPT5) offer higher precision than purely rule-based approaches, as evidenced by competitive results in both section title detection and deep level hierarchy assignments on legal textbooks.

4. Strengths and Limitations of TOC-Based and LLM-Based Approaches

The comparative evaluation in (Wehnert et al., 31 Aug 2025) demonstrates distinct regimes of effectiveness:

Approach	Strengths	Limitations
TOC-Based PageParser	Excels with accurate, complete TOC; competitive for deep hierarchies (levels 6–7)	Vulnerable to incomplete, misordered, or inaccurate TOC metadata; struggles with deeper or informal hierarchies
LLM-Refined PageParser	Robust to noisy, missing, or weak TOC; excels on intermediate/deeper levels (4–5)	Computationally more intensive; dependent on feature extraction for candidate heading quality

The hybrid XML-OCR-GPT5 approach achieved state-of-the-art precision on complex legal textbook segmentation, especially where TOC metadata was partial or unreliable. Pure TOC-based methods remain effective given high metadata fidelity, but are limited by TOC completeness and textual alignment discrepancies between TOC and full text.

5. Evaluation Metrics and Comparative Results

Several evaluation metrics are deployed:

• Section Title Detection: Precision and recall metrics with tolerant (edit-distance-based) matching.
• Hierarchy Level Allocation: Edit tree distance (Zhang-Shasha algorithm) is used for tree (hierarchy) similarity measurement.
• Section Boundary Assignment: Segmentation metrics such as Pk and WindowDiff gauge boundary accuracy versus ground truth.

Empirical results show that hybrid XML-OCR-GPT5 segmentation attains the lowest edit tree distances for deeply structured instances, outperforming open-source baselines (Docling, Marker) on challenging intermediate hierarchy levels.

6. Practical Applications and Implications

Robust hierarchical segmentation has broad impact in domains requiring precise document structure extraction:

• Knowledge Graph Construction: Segmenting legal textbooks into structured nodes (statutes, concepts, case examples) underpins rich knowledge graph linking, essential for legal informatics and semantic search.
• Enhanced Navigation: Accurate hierarchy enables academic libraries and digital readers to provide jump-to-section, breadcrumb tracing, and learning analytics features—critical in large educational resources.
• Semantic Data Extraction: Segment-aligned data supports summarization, citation, extraction, and topic modeling in workflow systems, enhancing their compatibility with legacy texts.
• Legacy and Non-Standard Format Processing: Multi-modal approaches bolster performance on older or non-markup documents that lack digital TOC or standardized formatting.

A plausible implication is that hybrid methods, tightly coupling structure-aware preprocessing with LLM-based semantic validation, will remain state-of-the-art for processing complex, multi-level documents in legal, technical, and educational corpora until fully digital, semantically tagged editorial pipelines are universal.

7. Summary

The HiPS framework (Wehnert et al., 31 Aug 2025) underscores that hierarchical section-wise writing and segmentation demand a multi-layered approach: TOC and XML-based heuristics for reliable structure when metadata is present, augmented by OCR-based spatial patterning to capture additional headings in weak-format documents, and finally LLM-based semantic filtering for robust noise reduction and hierarchy refinement. Evaluation demonstrates clear trade-offs and complementary strengths between these strategies, with the combined approach yielding superior extraction accuracy across complex, deeply nested document formats. Practical applications span knowledge graph construction, advanced document navigation, and content extraction in domains where document structure encodes critical semantic relationships.