@inproceedings{zheng-etal-2024-comprehensive,
    title = "A Comprehensive Survey on Document-Level Information Extraction",
    author = "Zheng, Hanwen  and
      Wang, Sijia  and
      Huang, Lifu",
    editor = "Tetreault, Joel  and
      Nguyen, Thien Huu  and
      Lamba, Hemank  and
      Hughes, Amanda",
    booktitle = "Proceedings of the Workshop on the Future of Event Detection (FuturED)",
    month = nov,
    year = "2024",
    address = "Miami, Florida, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/Author-Pages-WenzhengZhang-ZhengyanShi-ShuYang/2024.futured-1.6/",
    doi = "10.18653/v1/2024.futured-1.6",
    pages = "58--72",
    abstract = "Document-level information extraction (doc-IE) plays a pivotal role in the realm of natural language processing (NLP). This paper embarks on a comprehensive review and discussion of contemporary literature related to doc-IE. In addition, we conduct a thorough error analysis using state-of-the-art algorithms, shedding light on their limitations and remaining challenges for tackling the task of doc-IE. Our findings demonstrate that issues like entity coreference resolution and the lack of robust reasoning significantly hinder the effectiveness of document-level information extraction (doc-IE). Additionally, we uncover new challenges, including labeling noise and relation transitivity. The overarching objective of this survey paper is to provide valuable insights that can empower NLP researchers to further advance the performance of doc-IE."
}