## 2 Related Work
### Document-Level Event Extraction (DEE)  
In recent years, an increasing number of researchers have started to focus their attention on DEE tasks. Researchers have put forth numerous deep learning-based approaches to address the challenge of scattered arguments in DEE. The mainstream methods can be categorized into two types: trigger-inclusive methods and trigger-free methods. In trigger-inclusive ways, DCFEE (2018) introduced an event extraction model that utilizes a neural network sequence labeling model and a parameter-filling strategy. However, the lack of a comprehensive system for coherent reasoning without context fails to effectively address the challenge of scattered sense. [PERSON] et al. (2019) and [PERSON] et al. (2020) independently performed the joint extraction of events and entities within a document context. [PERSON] et al. utilized an external knowledge base, while [PERSON] et al. employed a Transformer model. Furthermore, the joint extraction approach should consider incorporating multi-task joint learning, effectively leveraging task interactions, and allowing tasks to benefit from the mutual learning process. MSAL (2018) focused on identifying all arguments and themes in the text, establishing their relationships to capture a more extensive context. Not long ago, DEEDP (2018) combined dependency paths with event role labels to identify events in financial documents. However, in the abovementioned models, the distance between event arguments and triggers increases within a document, making it more challenging for models to extract event elements [28; 29]. Additionally, some events may not have explicit triggers, or acquiring trigger information may be difficult. As a result, some researchers have attempted trigger-free DEE tasks. For instance, Doc2 EDAG [30] transformed event tables into entity-based directed acyclic graphs, enabling complex event-filling tasks to convert into multiple entity-based sequential path expansion subtasks, which are often easier to handle. However, this model's process of learning chain structures is computationally complex and not parallelizable, requiring substantial hardware power and time. DEPPN [31] introduced document-level encoders and multi-granularity decoders to generate event-aware representations parallelly with the document, enabling the simultaneous extraction of all events. GIT [32] proposed a Tracker module that continuously tracks extracted event records using a global memory module to capture interdependencies between events. MMR [33] built a multi-round multi-granularity reader designed to read multiple documents simultaneously at different granularities, such as words, sentences, and paragraphs. RAAT [34] utilized the association matrix and entity representation, which allowed it to get the relational dependency information.  
While some approaches have attained notable success, existing methods for DEE tasks rely on predefined event role orders for argument detection without considering the correlations between event roles or overlooking the overall information of the document. As a result, there is still a need for improvement in effectively capturing cross-sentence event relationships.