## 4 Experiments
### Results and Analysis  
We have chosen the current state-of-the-art DEE methods as baselines to enable comprehensive comparisons using the evaluation metrics introduced in Section 4.1. These baselines are listed below:  
DEPPN [31] employed a multi-granularity decoder to extract events simultaneously and utilized a matching loss function in training end-to-end models, leading to improved global optimization. GIT [32] introduced a tracking module to monitor and trace the extracted events, capturing the interdependencies between events. RAAT [34] proposed an attention-based transformer model that effectively captures the relationships between events in the document by incorporating a mechanism to enhance the representation of event relations. Tables 1 and 2 present the results of GNNDRS and the baselines on the two datasets, respectively. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **P (\%)** & **R (\%)** & **F1 (\%)** \\ \hline DEPPN [31] & 70.9 & 57.5 & 63.5 \\ GIT [32] & 71.3 & **86.3** & 78.1 \\ RAAT [34] & 70.7 & 64.5 & 67.4 \\ GNNDRS (ours) & **88.2** & 82.2 & **85.1** \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Precision (P), recall (R), and F1 scores of ER event extraction on the first dataset. Overall, our model significantly outperforms the baseline models. In Table 1, GNNDRS showed an impressive 7% increase in the F1 score compared to others. Similarly, in Table 2, GNNDRS achieved a 1.9% improvement in the F1 score. The improvement in the F1 score can be attributed to the implementation of two strategic approaches. First and foremost, we integrate localized and overarching semantic information by including the heterogeneous graph consisting of documents, sentences, and entity nodes. The local semantic data is captured by modeling distinct feature representations for each sentence and entity, and the document nodes provide global semantic insight. By combining them, our model can capture event information at a cross-sentence level. In addition, argument roles with fewer argument quantities may carry more critical semantic information. To ensure the accurate extraction of this crucial information, we adjust the detection order of argument roles, prioritizing these roles. Subsequently, the model progressively detects argument roles with more arguments. We split the datasets into two subsets to demonstrate the performance of our model in handling different types of documents. The first includes single-event documents (S.), which exclusively contain documents with a single event record. The second consists of multi-event documents (M.), which contain documents with multiple event records. This division allows us to assess how effectively our model captures and processes information from documents with varying complexities and event densities. Tables 3 and 4 compare GNNDRS and the baselines on the two datasets. In Table 3, GNNDRS performs better in the single-event extraction task and significantly outperforms the baseline model in the ER event type, with a 7.5% improvement in the F1 score for single events. In Table 4, GNNDRS outperforms the baseline model in the multiple extraction tasks, achieving a significant 0.6% improvement in terms of the F1 score. The results demonstrate that our proposed strategies effectively overcome the challenges of single-event extraction compared to existing baseline models. Firstly, we achieved global modeling of event information by introducing document nodes. Through