迈向通用语义分块：一种适用于超长文档的判别框架
Toward General Semantic Chunking: A Discriminative Framework for Ultra-Long Documents
AI总结：
本文提出基于Qwen3-0.6B的判别分割模型，通过跨窗口上下文融合和重叠滑动窗口策略，实现超长文档的高效段落边界检测，并提升下游检索效率。
摘要：
长文档主题分割在信息检索和文档理解中起着重要作用，但现有方法在超长文本设置中仍存在明显不足。传统判别模型受限于固定窗口，无法建模文档级语义；生成式大语言模型可以输出段落边界，但推理成本高且难以支持长输入。为解决这些问题，我们提出了一种基于Qwen3-0.6B的判别分割模型。在骨干网络之上，我们添加了跨窗口上下文融合层和边界分类头，并结合重叠滑动窗口策略。我们的模型支持单次输入高达13k个标记，可扩展至超长文档进行段落边界检测。为进一步提升下游检索效率，我们推导出一种向量融合方法，结合标量校正，将超长段落的表示压缩为单一向量，而无语义损失。在维基百科长文档主题分割数据集WIKI-727K上的实验表明，与基于Qwen2-0.5B的三个生成式模型相比，我们的方法在宏平均F1值上表现更优，并且推理速度快两序量级，显著提升了长文档处理的实用性和可扩展性。
英文摘要：
Long-document topic segmentation plays an important role in information retrieval and document understanding, yet existing methods still show clear shortcomings in ultra-long text settings. Traditional discriminative models are constrained by fixed windows and cannot model document-level semantics; generative large language models can output paragraph boundaries, but inference is expensive and long inputs are difficult to support. To address these issues, we propose a discriminative segmentation model based on Qwen3-0.6B. On top of the backbone network, we add a cross-window context fusion layer and a boundary classification head, and combine them with an overlapping sliding-window strategy. Our model supports single-pass inputs of up to 13k tokens and can be extended to ultra-long documents for paragraph boundary detection. To further enhance downstream retrieval efficiency, we derive a vector fusion method with scalar correction, which compresses the representation of ultra-long segments into a single vector without semantic loss. Experiments on the Wikipedia long-document topic segmentation dataset WIKI-727K show that, compared with three generative models based on Qwen2-0.5B released by Jina, our method achieves a better macro-averaged F1 and delivers two orders of magnitude faster inference, substantially improving the practicality and scalability of long-document processing.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
