标题： ABCD-LINK：跨文档细粒度链接的注释自举 显示英文标题

标题： ABCD-LINK: Annotation Bootstrapping for Cross-Document Fine-Grained Links

摘要： 理解文档之间的细粒度关系对于许多应用领域至关重要。然而，由于缺乏高效的方法来创建跨文档链接的训练和评估数据集，自动化辅助的研究受到限制。为了解决这个问题，我们引入了一个新的与领域无关的框架，用于从头开始选择最佳性能的方法并在新领域中标注跨文档链接。我们首先生成并验证了相互连接文档的半合成数据集。这些数据用于自动评估，生成最佳性能的链接方法清单。然后将这些方法用于广泛的人工评估研究，得出自然文本对的性能估计。我们在两个不同的领域——同行评审和新闻——中应用我们的框架，并表明结合检索模型和大语言模型（LLMs）可以从人工评分者那里获得78%的链接批准，这使强检索器单独的精度翻了一番。我们的框架使得在不同应用场景下系统地研究跨文档理解成为可能，而产生的新颖数据集为众多跨文档任务（如媒体框架和同行评审）奠定了基础。我们公开提供代码、数据和标注协议。 显示英文摘要

摘要： Understanding fine-grained relations between documents is crucial for many application domains. However, the study of automated assistance is limited by the lack of efficient methods to create training and evaluation datasets of cross-document links. To address this, we introduce a new domain-agnostic framework for selecting a best-performing approach and annotating cross-document links in a new domain from scratch. We first generate and validate semi-synthetic datasets of interconnected documents. This data is used to perform automatic evaluation, producing a shortlist of best-performing linking approaches. These approaches are then used in an extensive human evaluation study, yielding performance estimates on natural text pairs. We apply our framework in two distinct domains -- peer review and news -- and show that combining retrieval models with LLMs achieves 78\% link approval from human raters, more than doubling the precision of strong retrievers alone. Our framework enables systematic study of cross-document understanding across application scenarios, and the resulting novel datasets lay foundation for numerous cross-document tasks like media framing and peer review. We make the code, data, and annotation protocols openly available.