@inproceedings{ren-etal-2024-deie,
    title = "{DEIE}: Benchmarking Document-level Event Information Extraction with a Large-scale {C}hinese News Dataset",
    author = "Ren, Yubing  and
      Cao, Yanan  and
      Li, Hao  and
      Li, Yingjie  and
      Ma, Zixuan ZM  and
      Fang, Fang  and
      Guo, Ping  and
      Ma, Wei",
    editor = "Calzolari, Nicoletta  and
      Kan, Min-Yen  and
      Hoste, Veronique  and
      Lenci, Alessandro  and
      Sakti, Sakriani  and
      Xue, Nianwen",
    booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
    month = may,
    year = "2024",
    address = "Torino, Italia",
    publisher = "ELRA and ICCL",
    url = "https://aclanthology.org/2024.lrec-main.410",
    pages = "4592--4604",
    abstract = "A text corpus centered on events is foundational to research concerning the detection, representation, reasoning, and harnessing of online events. The majority of current event-based datasets mainly target sentence-level tasks, thus to advance event-related research spanning from sentence to document level, this paper introduces DEIE, a unified large-scale document-level event information extraction dataset with over 56,000+ events and 242,000+ arguments. Three key features stand out: large-scale manual annotation (20,000 documents), comprehensive unified annotation (encompassing event trigger/argument, summary, and relation at once), and emergency events annotation (covering 19 emergency types). Notably, our experiments reveal that current event-related models struggle with DEIE, signaling a pressing need for more advanced event-related research in the future.",
}

A text corpus centered on events is foundational to research concerning the detection, representation, reasoning, and harnessing of online events. The majority of current event-based datasets mainly target sentence-level tasks, thus to advance event-related research spanning from sentence to document level, this paper introduces DEIE, a unified large-scale document-level event information extraction dataset with over 56,000+ events and 242,000+ arguments. Three key features stand out: large-scale manual annotation (20,000 documents), comprehensive unified annotation (encompassing event trigger/argument, summary, and relation at once), and emergency events annotation (covering 19 emergency types). Notably, our experiments reveal that current event-related models struggle with DEIE, signaling a pressing need for more advanced event-related research in the future.
Ren, Yubing Cao, Yanan Li, Hao Li, Yingjie Ma, Zixuan ZM Fang, Fang Guo, Ping Ma, Wei
DEIE: Benchmarking Document-level Event Information Extraction with a Large-scale Chinese News Dataset