ABCD-LINK：跨文档细粒度链接的标注自举
ABCD-LINK: Annotation Bootstrapping for Cross-Document Fine-Grained Links
AI总结：
ABCD-LINK通过自举方法在同行评审和新闻领域实现跨文档细粒度链接的高效生成，结合检索模型与大语言模型，显著提升链接建议的批准率。
摘要：
理解文档间的细粒度链接对于许多应用至关重要，但进展受限于缺乏高效的数据整理方法。为解决这一限制，我们介绍了一个领域无关的框架，用于从头开始自举生成句子级别的跨文档链接。我们的方法（1）生成并验证了链接文档的半合成数据集，（2）利用这些数据集评估和筛选表现最佳的链接方法，（3）将筛选出的方法应用于大规模的人工标注过程中。我们将在两个不同的领域——同行评审和新闻中应用该框架，并展示结合检索模型与大语言模型的方法实现了73%的人类批准率，比仅使用强检索器的接受率高出一倍以上。我们的框架使用户能够生成新的数据集，从而系统研究跨文档理解，支持下游任务如媒体框架分析和同行评审评估。所有代码、数据和标注协议均已发布，以促进未来研究。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
