Text summarization via global structure awareness

2026-02-10

AI Summary

This work proposes GloSA-sum, a novel text summarization approach that addresses the limitations of existing methods—namely, their frequent neglect of global document structure, which undermines logical coherence, and the high computational cost of large language model–based alternatives. GloSA-sum is the first to integrate topological data analysis (TDA) into summarization by constructing a semantic-weighted graph and leveraging persistent homology to identify core semantics and logical backbones. It employs a preservation pooling mechanism to retain critical structural elements and combines a hierarchical strategy with a lightweight iterative process to efficiently handle long documents. Evaluated across multiple datasets, GloSA-sum significantly reduces redundancy while maintaining semantic fidelity and logical integrity, and further enhances the performance of downstream large language model tasks.

Technology Category

• Natural Language Processing: Summarization
• Data Mining & Knowledge Management: Data Visualization & Summarization
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Web Mining and Content Analysis: Normalization, clustering, classification, and summarization of Web text
• Search and Retrieval-Augmented AI: Web query analysis, representation and understanding

Abstract

Text summarization is a fundamental task in natural language processing (NLP), and the information explosion has made long-document processing increasingly demanding, making summarization essential. Existing research mainly focuses on model improvements and sentence-level pruning, but often overlooks global structure, leading to disrupted coherence and weakened downstream performance. Some studies employ large language models (LLMs), which achieve higher accuracy but incur substantial resource and time costs. To address these issues, we introduce GloSA-sum, the first summarization approach that achieves global structure awareness via topological data analysis (TDA). GloSA-sum summarizes text efficiently while preserving semantic cores and logical dependencies. Specifically, we construct a semantic-weighted graph from sentence embeddings, where persistent homology identifies core semantics and logical structures, preserved in a ``protection pool''as the backbone for summarization. We design a topology-guided iterative strategy, where lightweight proxy metrics approximate sentence importance to avoid repeated high-cost computations, thus preserving structural integrity while improving efficiency. To further enhance long-text processing, we propose a hierarchical strategy that integrates segment-level and global summarization. Experiments on multiple datasets demonstrate that GloSA-sum reduces redundancy while preserving semantic and logical integrity, striking a balance between accuracy and efficiency, and further benefits LLM downstream tasks by shortening contexts while retaining essential reasoning chains.

Problem

• text summarization
• global structure
• long-document processing
• coherence
• computational efficiency

Innovation

• topological data analysis
• global structure awareness
• persistent homology
• text summarization
• hierarchical summarization