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arXiv · 2508.20778

SEAL: Structure and Element Aware Learning to Improve Long Structured Document Retrieval

Xinhao Huang·Zhibo Ren·Yipeng Yu·Ying Zhou·Zulong Chen·Zeyi Wen
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Abstract

In long structured document retrieval, existing methods typically fine-tune pre-trained language models (PLMs) using contrastive learning on datasets lacking explicit structural information. This practice suffers from two critical issues: 1) current methods fail to leverage structural features and element-level semantics effectively, and 2) the lack of datasets containing structural metadata. To bridge these gaps, we propose \our, a novel contrastive learning framework. It leverages structure-aware learning to preserve semantic hierarchies and masked element alignment for fine-grained semantic discrimination. Furthermore, we release \dataset, a long structured document retrieval dataset with rich structural annotations. Extensive experiments on both released and industrial datasets across various modern PLMs, along with online A/B testing, demonstrate consistent performance improvements, boosting NDCG@10 from 73.96\% to 77.84\% on BGE-M3. The resources are available at https://github.com/xinhaoH/SEAL.

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Xinhao Huang, Zhibo Ren, Yipeng Yu, Ying Zhou, Zulong Chen, Zeyi Wen. 2025-08-31. SEAL: Structure and Element Aware Learning to Improve Long Structured Document Retrieval. https://arxiv.org/abs/2508.20778

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