@inproceedings{basch-etal-2026-abcd,
    title = "{ABCD}-{LINK}: Annotation Bootstrapping for Cross-Document Fine-Grained Links",
    author = "Basch, Serwar  and
      Kuznetsov, Ilia  and
      Hope, Tom  and
      Gurevych, Iryna",
    editor = "Demberg, Vera  and
      Inui, Kentaro  and
      Marquez, Llu{\'i}s",
    booktitle = "Proceedings of the 19th Conference of the {E}uropean Chapter of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
    month = mar,
    year = "2026",
    address = "Rabat, Morocco",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-eacl/2026.eacl-long.157/",
    pages = "3399--3423",
    ISBN = "979-8-89176-380-7",
    abstract = "Understanding fine-grained links between documents is crucial for many applications, yet progress is limited by the lack of efficient methods for 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."
}