## 1 Introduction  
Event Extraction (EE) aims to extract structured event details from unstructured text [1], which is regarded as a core research area of Natural Language Processing (NLP). Generally, event details include event types, trigger words, and the various arguments that play different roles within events. The structured representation of events facilitates many downstream tasks, such as recommendation systems [2, 3], knowledge graph construction [4, 5], and intelligent question-answering systems [6, 7]. Particularly in food and cosmetics sentiment monitoring, researchers have identified immense potential in utilizing news and social media data [8] for event extraction in sentiment analysis. Moreover, previous studies have indicated that harnessing multiple data sources [9] or incorporating different features [10] can facilitate the harmful events from extensive text data. Such capabilities offer valuable insights and guidance for sentiment management and decision-making processes. Existing methods [11, 12, 13, 14, 15] of EE are mainly focused on sentence-level event extraction (SEE), which involves extracting events and arguments from individual sentences. For example, ACE-2005 [16], one of the most frequently utilized datasets for evaluation, only annotate event arguments within the scope of a sentence. However, in real-world scenarios, event arguments often span multiple sentences, causing incomplete results when extracting from individual sentences. In reality, a document can contain multiple event records, an entity can play multiple argument roles, and event records may have missing event arguments. Figure 1 illustrates an example from a financial document containing two event records, an Equity Pledge (EP) event and an Equity Underweight (EU) event. In the EP event, the Kangping Company plays the role of the Pledge, which refers to a corporate entity, and it fills this entity as an event argument for the Pledge role in the EP event. Atthe same time, the Kangping Company also plays the argument role of Equity Holder in the EU event. However, the event record for the EU event lacks event arguments for the argument role of Later Holding Shares and Average Price. Therefore, Document-level Event Extraction (DEE) is necessary in such cases. The primary objective of the DEE task is to extract events that encompass specific argument roles from an entire document, potentially spanning multiple sentences. There are two main areas for improvement in DEE tasks. One is failing to capture long-distance dependencies between sentences and needing a comprehensive perspective when capturing the correlation between arguments across sentences. Secondly, the extraction order of argument roles is often predefined when performing an asynchronous extraction. This rigid extraction strategy may make it hard to handle the diverse occurrence order and importance of different arguments within each event. Hence, we propose a novel model of Graph Neural Networks and Dynamic Role Sorting (GNNDRS) for argument extraction in documents. When dealing with scattered arguments across sentences, we establish interactions between documents, sentences, and entities. Specifically, we construct a heterogeneous graph interaction network, including document nodes, sentence nodes, and entity nodes. We model their interactions using five types of edges: sentence-node edges, sentence-entity edges, intra-sentence entity-node edges, cross-sentence entity-node edges, and document-sentence edges. Regarding argument role detection, we dynamically adjust the detection order based on the number of arguments. We prioritize roles with fewer arguments and gradually transition to roles with more arguments. The results demonstrate the effectiveness of our proposed approach compared to the baseline models. Our contributions can be summarized as follows:  
1. We propose the GNNDRS model for addressing the two mentioned challenges.