@inproceedings{kar-etal-2022-arggen,
    title = "{A}rg{G}en: Prompting Text Generation Models for Document-Level Event-Argument Aggregation",
    author = "Kar, Debanjana  and
      Sarkar, Sudeshna  and
      Goyal, Pawan",
    editor = "He, Yulan  and
      Ji, Heng  and
      Li, Sujian  and
      Liu, Yang  and
      Chang, Chua-Hui",
    booktitle = "Findings of the Association for Computational Linguistics: AACL-IJCNLP 2022",
    month = nov,
    year = "2022",
    address = "Online only",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2022.findings-aacl.37/",
    doi = "10.18653/v1/2022.findings-aacl.37",
    pages = "399--404",
    abstract = "Most of the existing discourse-level Information Extraction tasks have been modeled to be extractive in nature. However, we argue that extracting information from larger bodies of discourse-like documents requires more natural language understanding and reasoning capabilities. In our work, we propose the novel task of document-level event argument aggregation which generates consolidated event-arguments at a document-level with minimal loss of information. More specifically, we focus on generating precise document-level information frames in a multilingual setting using prompt-based methods. In this paper, we show the effectiveness of u prompt-based text generation approach to generate document-level argument spans in a low-resource and zero-shot setting. We also release the first of its kind multilingual event argument aggregation dataset that can be leveraged in other related multilingual text generation tasks as well: \url{https://github.com/DebanjanaKar/ArgGen}."
}