## 3 Methodology
### Argument Extraction  
We adopted an ordered expansion tree [32] to decode documents containing multiple event records and extract specific types of event records. After detecting the event types, we performed argument role detection by dynamically adjusting the detection order based on the obtained vector representations and labels of all candidate entities in the text. We first detected roles with fewer arguments and gradually transitioned to argument roles with more arguments. The process of filling event records, as shown in Figure 4, analyses five argument roles. The Company Name, Highest Trading Price, and Lowest Trading Price have only one event argument, while the Repurchased Shares and Closing Date argument roles have two event arguments. When filling in event records, prioritize identifyingargument roles with fewer associated arguments and gradually shift towards roles with more event arguments. Therefore, these two argument roles will be detected last.  
Specifically, when extracting arguments, we start from a virtual root node and expand based on the reordered sequence of argument roles. We introduce the Tracker module [32], and the path from the root node to the leaf node is an event record. For the i-th record path represented by an entity sequence \(\text{U}_{\text{i}}=[\text{E}_{\text{i1}},\text{E}_{\text{i2}},\ldots]\), the Tracker utilizes LSTM to encode this vector and adds the event type embedding. The compressed information is then stored in the global memory \(\text{G}_{\text{i}}\) for sharing across different event types, as shown in Figure 5. It represents the decoding process, wherein records are extracted in a revised sequence following the adjustment of argument role detection. Two event records have already been retrieved from the document. By harnessing the contained information within the real-time Tracker and its globally tracked memory, accurate forecasts can be made regarding the argument roles associated with each event. In this prediction, Entity B is assigned to Role1, and the subsequent argument roles are forecasted accordingly, commencing from this particular child node until a comprehensive event record is extracted.  
During the inference process, we predict the k-th role by incorporating the feature of the argument role into the entity representation.  
\[\overline{E}=E+Role_{k}, \tag{10}\]  
Figure 4: The reordering process of argument extraction.  
Figure 5: The decoding process. Capitalized letters (A–D) embody all potential entities, whereas S1–S3 denote the distinctive features of the sentence.  
where \(Role_{k}\) is the embedding of the k-th role, \(E\) is the feature matrix of the entities at the previous time, and \(\overline{E}\) refers to the feature matrix at the current time.  
Next, the Tracker concatenates the entity feature matrix \(\overline{E}\), sentence feature matrix \(S\), current record path \(U_{i}\), and global memory \(G_{i}\). It then utilizes the Transformer to update the feature information. The updated features include specific role information for all candidate entities globally.  
\[\left[\widetilde{E},\widetilde{S},\widetilde{U}_{i},\widetilde{G}\right]=Transformer \big{(}\big{[}\overline{E};S;U_{i};G\big{]}\big{)}, \tag{11}\]  
\(L_{record}\) is represented as:  
\[L_{record}=-\sum_{n=1}^{N}\sum_{i=1}logP(y^{i,n}|n), \tag{12}\]  
where \(N\) is the node in the event record tree, and \(y^{i,n}\) refers to its golden label sequences. If the i-th entity is the next event argument of node \(n\), then \(y^{i,n}=1\), otherwise \(y^{i,n}=0\).