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

• URL: http://arxiv.org/abs/2602.23370v1
• Date: Tue, 23 Dec 2025 16:16:51 GMT
• Title: Toward General Semantic Chunking: A Discriminative Framework for Ultra-Long Documents
• Abstract summary: Long-document topic segmentation plays an important role in information retrieval and document understanding.<n>Traditional discriminative models are constrained by fixed windows and cannot model document-level semantics.<n>generative large language models can output paragraph boundaries, but inference is expensive and long inputs are difficult to support.
• Score: 10.682600460180174
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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