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        <title>ABCD-LINK: Annotation Bootstrapping for Cross-Document Fine-Grained Links</title>
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        <namePart type="given">Serwar</namePart>
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        <namePart type="given">Ilia</namePart>
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    <name type="personal">
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        <namePart type="family">Gurevych</namePart>
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            <title>Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)</title>
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            <namePart type="given">Vera</namePart>
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            <namePart type="given">Lluís</namePart>
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    <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.</abstract>
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%0 Conference Proceedings
%T ABCD-LINK: Annotation Bootstrapping for Cross-Document Fine-Grained Links
%A Basch, Serwar
%A Kuznetsov, Ilia
%A Hope, Tom
%A Gurevych, Iryna
%Y Demberg, Vera
%Y Inui, Kentaro
%Y Marquez, Lluís
%S Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 March
%I Association for Computational Linguistics
%C Rabat, Morocco
%@ 979-8-89176-380-7
%F basch-etal-2026-abcd
%X 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.
%U https://aclanthology.org/2026.eacl-long.157/
%P 3399-3423

Markdown (Informal)

[ABCD-LINK: Annotation Bootstrapping for Cross-Document Fine-Grained Links](https://aclanthology.org/2026.eacl-long.157/) (Basch et al., EACL 2026)

• ABCD-LINK: Annotation Bootstrapping for Cross-Document Fine-Grained Links (Basch et al., EACL 2026)

ACL

• Serwar Basch, Ilia Kuznetsov, Tom Hope, and Iryna Gurevych. 2026. ABCD-LINK: Annotation Bootstrapping for Cross-Document Fine-Grained Links. In Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers), pages 3399–3423, Rabat, Morocco. Association for Computational Linguistics.