ABCD-LINK: Annotation Bootstrapping for Cross-Document Fine-Grained Links
Fuente:
arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866911398578094080 |
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| author | Basch, Serwar Kuznetsov, Ilia Hope, Tom Gurevych, Iryna |
| author_facet | Basch, Serwar Kuznetsov, Ilia Hope, Tom Gurevych, Iryna |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_01387 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | ABCD-LINK: Annotation Bootstrapping for Cross-Document Fine-Grained Links Basch, Serwar Kuznetsov, Ilia Hope, Tom Gurevych, Iryna Computation and Language Information Retrieval Machine Learning 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. |
| title | ABCD-LINK: Annotation Bootstrapping for Cross-Document Fine-Grained Links |
| topic | Computation and Language Information Retrieval Machine Learning |
| url | https://arxiv.org/abs/2509.01387 |