@inproceedings{huang-etal-2025-seal,
    title = "{SEAL}: Structure and Element Aware Learning Improves Long Structured Document Retrieval",
    author = "Huang, Xinhao  and
      Ren, Zhibo  and
      Yu, Yipeng  and
      Zhou, Ying  and
      Chen, Zulong  and
      Wen, Zeyi",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.emnlp-main.429/",
    pages = "8537--8547",
    ISBN = "979-8-89176-332-6",
    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 SEAL, 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 StructDocRetrieval, a long structured document retrieval dataset with rich structural annotations. Extensive experiments on both the released and industrial datasets across various modern PLMs, and online A/B testing demonstrate consistent improvements, boosting NDCG@10 from 73.96{\%} to 77.84{\%} on BGE-M3. The resources are available at https://github.com/xinhaoH/SEAL."
}