Efficient Document Segmentation with Qwen3-0.6B

Long-document topic segmentation plays a crucial role in information retrieval and document understanding. However, existing methods struggle with particularly long texts. Traditional discriminative models are constrained by fixed windows, while generative large language models (LLMs), although capable of identifying paragraph boundaries, are expensive in terms of inference and difficult to adapt to very long inputs.

To address these issues, a discriminative segmentation model based on Qwen3-0.6B has been proposed. This model integrates a cross-window context fusion layer and a boundary classification head, combined with an overlapping sliding-window strategy. The system supports inputs of up to 13,000 tokens in a single pass and can be extended to even longer documents for paragraph boundary detection.

Optimization for Downstream Retrieval

To further improve the efficiency of downstream retrieval, a vector fusion method with scalar correction has been developed. This approach compresses the representation of ultra-long segments into a single vector, minimizing the loss of semantic information. Tests on the WIKI-727K dataset, dedicated to the segmentation of long Wikipedia documents, demonstrate that the proposed model outperforms three generative models based on Qwen2-0.5B in terms of macro-averaged F1-score, while offering an inference that is two orders of magnitude faster. This improvement significantly increases the practicality and scalability in processing large documents.