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.
Abstract (translated)
文本摘要在自然语言处理（NLP）中是一项基础任务，信息爆炸使得长文档的处理日益复杂，从而突显了自动摘要的重要性。现有的研究主要集中在模型改进和句子级修剪上，但往往忽略了全局结构，导致摘要连贯性受损并削弱下游性能表现。一些研究使用大型语言模型（LLMs），虽然提高了准确性，但是带来了高昂的时间与资源成本。为了应对这些问题，我们提出了GloSA-sum方法，这是首个通过拓扑数据分析实现全局结构感知的文本总结技术。 GloSA-sum在保持语义核心和逻辑依赖的同时高效地生成摘要。具体而言，该方法构建了一个基于句子嵌入的语义加权图，在此图中，持久同调性（Persistent Homology）用于识别关键语义及逻辑结构，并将这些结构保存在一个称为“保护池”的部分以供后续摘要过程使用。我们设计了一种拓扑引导迭代策略：利用轻量级代理指标来估算句子的重要性，从而避免重复计算高成本的复杂度问题，在提高效率的同时保持结构完整性和连贯性。 为了进一步增强对长文本处理的能力，我们提出了一个分层策略，该策略结合了段落级别的和全局的总结技术。在多个数据集上的实验表明，GloSA-sum能够减少冗余，并且保留语义及逻辑的一致性，在准确度与效率之间找到平衡点；同时它还能通过缩短上下文长度而保持关键推理链的方式进一步提升大型语言模型下游任务的表现。
URL
https://arxiv.org/abs/2602.09821
