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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2508.20778 |
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| _version_ | 1866916927312494592 |
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| author | Huang, Xinhao Ren, Zhibo Yu, Yipeng Zhou, Ying Chen, Zulong Wen, Zeyi |
| author_facet | Huang, Xinhao Ren, Zhibo Yu, Yipeng Zhou, Ying Chen, Zulong Wen, Zeyi |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_20778 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | SEAL: Structure and Element Aware Learning to Improve Long Structured Document Retrieval Huang, Xinhao Ren, Zhibo Yu, Yipeng Zhou, Ying Chen, Zulong Wen, Zeyi Information Retrieval Machine Learning 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. |
| title | SEAL: Structure and Element Aware Learning to Improve Long Structured Document Retrieval |
| topic | Information Retrieval Machine Learning |
| url | https://arxiv.org/abs/2508.20778 |