@inproceedings{lorandi-etal-2026-automatic,
    title = "Automatic Paper Analysis and Categorisation for Systematic Reviews with Combined Reasoning-Augmented {SFT} and {DAPO} {RL}",
    author = "Lorandi, Michela  and
      Belz, Anya  and
      Mille, Simon  and
      Thomson, Craig",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.findings-acl.2022/",
    pages = "40676--40699",
    ISBN = "979-8-89176-395-1",
    abstract = "Systematic reviews are a cornerstone of modern science, synthesising evidence from published research to provide the highest level of research evidence in a field. The process includes categorising studies on a number of different dimensions which is laborious and time consuming. Automatic approaches are beginning to be explored but the complexity of the task means we are currently far from a satisfactory solution. In this paper, we test different annotation scheme-agnostic methods for automatic NLP paper categorisation for systematic reviews, and test them on two tasks: (i) annotating NLP papers for categories of reported controlled-text generation methods, and (ii) annotating NLP papers for categories of reported human evaluations. We find that reasoning-enhanced fine-tuning combined with DAPO reinforcement learning rewarding both correctness and output format substantially improves the performance of LLMs (by up to +53.8 points), even when they have been pre-trained to perform reasoning, and cuts time required for annotation by around 80{\%} in a human-in-the-loop setting."
}