用自举方法从零构建跨文档细粒度链接数据集，提升链接准确率。
ABCD-LINK: Annotation Bootstrapping for Cross-Document Fine-Grained Links
- 通过生成半合成数据集验证并筛选最佳链接方法。
- 结合检索模型与大模型，人工审批通过率达73%。
- 适用于新闻、同行评审等场景，支持后续分析任务。
理解文档间的细粒度关联对诸多应用至关重要，但受限于高效的数据整理方法。为此，我们提出一种无领域依赖的自举框架，可从零开始构建句子级跨文档链接数据集。该方法首先生成并验证半合成的链接文档数据集，其次利用这些数据集评估并筛选出表现最佳的链接方法，最后在大规模人机协同标注中应用优选方法处理真实文本对。我们在同行评审和新闻两个不同领域中应用该框架，发现将检索模型与大语言模型结合，所提链接的人工审批通过率达到73%，超过仅使用强检索器的两倍以上。该框架使用户能够创建新数据集，系统研究跨文档理解，支持媒体框架分析、同行评审评估等下游任务。所有代码、数据及标注协议均已开源，以推动后续研究。
原文摘要 · Abstract (English)
Understanding fine-grained links between documents is crucial for many applications, yet progress is limited by the lack of efficient methods for data curation. To address this limitation, we introduce a domain-agnostic framework for bootstrapping sentence-level cross-document links from scratch. Our approach (1) generates and validates semi-synthetic datasets of linked documents, (2) uses these datasets to benchmark and shortlist the best-performing linking approaches, and (3) applies the shortlisted methods in large-scale human-in-the-loop annotation of natural text pairs. We apply the framework in two distinct domains -- peer review and news -- and show that combining retrieval models with LLMs achieves a 73% human approval rate for suggested links, more than doubling the acceptance of strong retrievers alone. Our framework allows users to produce novel datasets that enable systematic study of cross-document understanding, supporting downstream tasks such as media framing analysis and peer review assessment. All code, data, and annotation protocols are released to facilitate future research.
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