@inproceedings{islam-etal-2026-docsplit,
    title = "{D}oc{S}plit: A Comprehensive Benchmark Dataset and Evaluation Approach for Document Packet Recognition and Splitting",
    author = "Islam, Md Mofijul  and
      Salekin, Md Sirajus  and
      Balakrishnan, Nivedha  and
      III, Vincil C. Bishop  and
      Jain, Niharika  and
      Romo, Spencer  and
      Strahan, Bob  and
      Xie, Boyi  and
      Socolinsky, Diego A.",
    editor = "Li, Yunyao  and
      Rehm, Georg  and
      Tu, Mei",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics ({ACL} 2026)",
    month = jul,
    year = "2026",
    address = "San Diego, California, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.acl-industry.12/",
    pages = "159--183",
    ISBN = "979-8-89176-394-4",
    abstract = "Document understanding in real-world applications often requires processing heterogeneous, multi-page document packets containing multiple documents stitched together. Despite recent advances in visual document understanding, the fundamental task of document packet splitting, which involves separating a document packet into individual units, remains largely unaddressed. We present the first comprehensive benchmark dataset, $\textit{DocSplit}$, along with novel evaluation metrics for assessing the document packet splitting capabilities of large language models. $\textit{DocSplit}$ comprises five datasets of varying complexity, covering diverse document types, layouts, and multimodal settings. We formalize the $\textit{DocSplit}$ task, which requires models to identify document boundaries, classify document types, and maintain correct page ordering within a document packet. The benchmark addresses real-world challenges, including out-of-order pages, interleaved documents, and documents lacking clear demarcations. We conduct extensive experiments evaluating multimodal LLMs on our datasets, revealing significant performance gaps in current models' ability to handle complex document splitting tasks. The $\textit{DocSplit}$ benchmark datasets and proposed novel evaluation metrics provide a systematic framework for advancing document understanding capabilities essential for legal, financial, healthcare, and other document-intensive domains. We release the datasets and evaluation code to facilitate future research in document packet processing."
}