# Chinese Document-Level Emergency Event Extraction Dataset and Corresponding Methods
## 1 Introduction  
In the real world, emergency events [1], such as traffic accidents, fires, forest fires, earthquakes, and health hazards, pose a serious threat to human life and property. As a result, they garner widespread concern from people across various sectors. In the context of the Internet information era, the rapid spread and fermentation of information about emergency events through online media will breed public opinion events and affect social public safety. Accurate and efficient access to structured information about emergency events can assist relevant personnel in early detection and prompt response to such events, mitigate the emergence of public opinion incidents, and uphold societal safety. The goal of event extraction is to identify pre-specified types of events and their corresponding event arguments from plain text. Numerous previous studies have primarily focused on sentence-level event extraction [2; 3; 4], with most of these studies evaluating the ACE dataset [5]. These sentence-level approaches make predictions within a single sentence and are incapable of extracting events that span multiple sentences. In the real world, the information of event arguments cannot be fully obtained from a single sentence. For example, as shown in Figure 1, the event argument of the \"Accident\" event \" \" (Chongqing Yongchuan Diaoshiudong coal mine) and \"12 / 14 / 17 (around 17:00 on 4 December) are distributed in two different sentences, s1 and s2, so we devoted ourselves to the study of DEE. To extract structured event information from documents, researchers have proposed numerous model methods and datasets for model training and validation. In the following sections, we present the DEE datasets and DEE models and methods that have been put forth in previous works. A wide range of research scholars have conducted extensive work on DEE datasets to train and validate DEE model methods. They have constructed numerous DEE datasets, such as the MUC-4 [6] dataset. This dataset consists of 1700 documents annotated using an associated role-population template. Additionally, the Twitter dataset was constructed by collecting and annotating English tweets posted in December 2010. It includes 20 event types and 1000 tweets. The WIKIEVENTS dataset, published by [PERSON] et al. [7], as a document-level benchmark dataset, utilizes English Wikipedia articles as the data source. [PERSON] et al. [8] performed experiments on four types of financial events, namely equity freeze events, equity pledge events, equity repurchase events, and equity increase events. They tagged a total of 2976 announcements. Although numerous DEE datasets have been constructed by domestic and foreign researchers in previous works, there are two issues. Firstly, most of these datasets are in English, making it impossible to train and validate Chinese DEE model methods. Secondly, there is a lack of DEE datasets specifically constructed for the field of emergency events. Hence, the current priority is to develop Chinese document-level emergency event extraction datasets and address the issue of missing datasets. Regarding DEE models and methods, numerous scholars have directed their focus towards addressing two challenges: argument scattering and multiple events. Specifically, argument scattering refers to the situation where event arguments are dispersed across multiple sentences in a document. For instance, as shown in Figure 1, the event arguments \"(Chongqing Yongchuan Diaoshiudong coal mine) and \"30 \"(30 h) for the \"Rescue\" event are found in two different sentences, s1 and s5, within the document. On the other hand, multiple events indicate that a document can encompass various distinct events. For example, Figure 1 includes three different events: \"InjureDead\", \"Rescue\", and \"Accident\". In a previous study, [PERSON] et al. [8] proposed the DCFEE model, which extracts trigger words and arguments in a sentence-by-sentence manner.