@inproceedings{ilgen-etal-2026-vaxscope,
    title = "{V}ax{S}cope: Document-Level Structured Evidence Extraction from Immunization Systematic Reviews",
    author = "Ilgen, Bahar  and
      Awotoro, Ebenezer  and
      Hattab, Georges",
    editor = "Demner-Fushman, Dina  and
      Ananiadou, Sophia  and
      Roberts, Kirk  and
      Tsujii, Junichi",
    booktitle = "{B}io{NLP} 2026",
    month = jul,
    year = "2026",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl-workshops/2026.bionlp-1.69/",
    pages = "853--863",
    ISBN = "979-8-89176-434-7",
    abstract = "Systematic reviews are fundamental to evidence-based medicine, but the clinical evidence they contain is primarily expressed in unstructured text, making large-scale extraction and reuse difficult. Existing biomedical NLP methods have achieved strong performance on span-level extraction from clinical trials and abstracts; however, these approaches are insufficient for systematic reviews, where evidence is often distributed across multiple studies, sentences, and sections and must be aggregated into normalized document-level attributes. We introduce VaxScope, a benchmark dataset for document-level structured evidence extraction from immunization-related systematic reviews. VaxScope is constructed through an expert-guided semi-automatic annotation pipeline that combines automatic candidate generation with domain expert validation to ensure consistency and annotation quality. We formalize the task as document-level structured extraction, where target labels are defined at the review level and require aggregating evidence beyond isolated textual spans. We further establish baselines for document-level structured extraction using abstract-level input representations and evaluate how access to evidence-grounded contextual input improves performance over abstract-only settings. Baseline experiments show that PubMedBERT achieves the best overall performance (Avg F1: 0.850), with evidence-grounded input improving performance particularly for fields requiring distributed contextual reasoning."
}