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Toward General Semantic Chunking: A Discriminative Framework for Ultra-Long Documents

arXiv:2602.23370v1 Announce Type: cross Abstract: 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, whi

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Kaifeng Wu, Junyan Wu, Qiang Liu, Jiarui Zhang, Wen Xu · · 1 min read · 8 views
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arXiv:2602.23370v1 Announce Type: cross Abstract: 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.

Executive Summary

This article proposes a novel discriminative framework for ultra-long document segmentation, addressing the shortcomings of existing methods. The proposed framework, built upon Qwen3-0.6B, incorporates a cross-window context fusion layer and a boundary classification head, and utilizes an overlapping sliding-window strategy. This enables the model to support single-pass inputs of up to 13k tokens and facilitates efficient inference. Additionally, the article introduces a vector fusion method with scalar correction, which compresses ultra-long segment representations into a single vector without semantic loss. Experiments on the WIKI-727K dataset demonstrate the model's superiority in macro-averaged F1 and inference speed, making it a promising solution for long-document processing.

Key Points

  • ▸ Proposed discriminative framework addresses limitations of existing ultra-long document segmentation methods
  • ▸ Cross-window context fusion layer and boundary classification head improve model performance
  • ▸ Overlapping sliding-window strategy enables efficient inference for ultra-long documents

Merits

Strength

The proposed framework demonstrates substantial improvements in macro-averaged F1 and inference speed, making it a promising solution for long-document processing.

Flexibility

The model's ability to support single-pass inputs of up to 13k tokens and facilitate efficient inference makes it a versatile solution for various applications.

Scalability

The framework's design enables easy extension to ultra-long documents, making it a scalable solution for large-scale document processing.

Demerits

Limitation

The proposed framework is built upon a specific backbone network (Qwen3-0.6B), which may limit its applicability to other models or datasets.

Complexity

The model's architecture, incorporating multiple layers and strategies, may increase its computational complexity and require significant resources for training and inference.

Expert Commentary

The article presents a well-designed and comprehensive framework for ultra-long document segmentation. The proposed method's ability to improve macro-averaged F1 and inference speed is a significant contribution to the field. However, the model's reliance on a specific backbone network and its complexity may limit its applicability and scalability. Further research is needed to explore the framework's potential in various applications and to address its limitations.

Recommendations

  • ✓ Further experimentation with different backbone networks and architectures to assess the framework's generalizability and scalability.
  • ✓ Investigation of the framework's potential applications in various domains, such as document analysis, information retrieval, and text summarization.

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