@inproceedings{kaur-etal-2026-adapting,
    title = "Adapting {A}uto{ARGUE} for Automatic Report Evaluation under Missing Citation Annotations",
    author = "Kaur, Divrose  and
      Bedi, Jatin  and
      Singh, Jasmeet",
    editor = "Yang, Eugene  and
      Lawrie, Dawn  and
      MacAvaney, Sean  and
      Mayfield, James  and
      Soldaini, Luca  and
      Yates, Andrew",
    booktitle = "Proceedings of the 1st Workshop on Multilingual Report Generation via Retrieval Augmented Generation ({RAG}4{R}eports 2026)",
    month = jul,
    year = "2026",
    address = "San Diego, CA, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl-workshops/2026.rag4reports-1.15/",
    pages = "103--107",
    ISBN = "979-8-89176-417-0",
    abstract = "We adapt the AutoARGUE framework (Walden et al., 2026) for Task A.2 of RAG4Reports 2026, which requires ranking 57 report generation systems across 68 topics using automated evaluation. The RAGTIME-1 corpus poses a fundamental challenge: all nugget annotations use a no-reference-doc sentinel rather than ground-truth document citations, rendering the original citation-relevance gating inoperable. We address this with three adaptations: automatic sentinel detection with forced direct LLM-based nugget matching; a WEAK POSITIVE partial credit mechanism for sentences that correctly answer nuggets but lack attesting citations; and a report-level request alignment check. Our nugget{\_}coverage{\_}weighted metric achieves the highest topic-level Pearson correlation (r=0.599) of any non-coordinator submission, closely approaching the coordinator baseline (r=0.607)."
}