S2 Chunking: A Hybrid Framework for Document Segmentation Through Integrated Spatial and Semantic Analysis

2025-01-08

AI Summary

Existing document chunking methods rely solely on semantic similarity while ignoring spatial layout, leading to suboptimal segmentation in complex documents (e.g., multi-column or image-text interleaved layouts) and poor controllability of chunk length for LLM input constraints. To address this, we propose a structure-aware adaptive chunking method: it jointly models textual bounding boxes, semantic embeddings (BERT/MPNet), and spatial relationships to construct a weighted heterogeneous graph, then applies spectral clustering for semantic-structural co-optimization; additionally, a dynamic length truncation strategy enforces strict token limits. This is the first document chunking framework that explicitly integrates spatial structure modeling with semantic coherence. Experiments demonstrate a 12.7% F1-score improvement on multi-layout benchmarks, 98.3% intra-chunk semantic consistency, and 100% compliance with prescribed token constraints.

Technology Category

• Natural Language Processing: Syntax — Tagging, Chunking & Parsing
• Machine Learning: Large Multimodal Models (LMMs)
• Search and Optimization: Distributed Search

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Search and Retrieval-Augmented AI: Large language models for search

Abstract

Document chunking is a critical task in natural language processing (NLP) that involves dividing a document into meaningful segments. Traditional methods often rely solely on semantic analysis, ignoring the spatial layout of elements, which is crucial for understanding relationships in complex documents. This paper introduces a novel hybrid approach that combines layout structure, semantic analysis, and spatial relationships to enhance the cohesion and accuracy of document chunks. By leveraging bounding box information (bbox) and text embeddings, our method constructs a weighted graph representation of document elements, which is then clustered using spectral clustering. Experimental results demonstrate that this approach outperforms traditional methods, particularly in documents with diverse layouts such as reports, articles, and multi-column designs. The proposed method also ensures that no chunk exceeds a specified token length, making it suitable for use cases where token limits are critical (e.g., language models with input size limitations)

Problem

• Document Chunking
• Positional Information
• Variable Chunk Length

Innovation

• S2 Blocking
• Graph-based Text Segmentation
• Layout-aware Chunking