## 4 Experiments
### Results and Analysis
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**EF**} & \multicolumn{2}{c}{**ER**} & \multicolumn{2}{c}{**EU**} & \multicolumn{2}{c}{**EO**} & \multicolumn{2}{c}{**EP**} & \multicolumn{2}{c}{**Average**} & \multicolumn{2}{c}{**Total**} \\  & **S.** & **M.** & **S.\%** & **M.** & **S.** & **M.** & **S.** & **M.** & **S.** & **M.** & **S.** & **M.** & **S.** & **M.** & **S.** & **M.** \\  & **(\%)** & **(\%)** & **S.** & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** \\ \hline DEPPN [31] & 56.9 & 37.7 & 65.3 & 51.6 & 55.4 & 43.6 & 46.0 & 41.4 & 60.2 & 47.4 & 56.8 & 44.3 & 60.1 & 46.3 \\ GIT [32] & 59.6 & 45.7 & **70.0** & **58.4** & 61.1 & **46.2** & 66.1 & **54.0** & 76.8 & 54.2 & 66.7 & **51.7** & **71.2** & 53.4 \\ RAAT [34] & 56.7 & 39.3 & 53.3 & 46.5 & 54.4 & 42.8 & 55.2 & 44.0 & 67.7 & 55.2 & 57.5 & 45.5 & 59.6 & 52.4 \\ GNNDRS & **62.2** & **47.4** & 66.1 & 53.2 & **63.9** & 42.0 & **66.9** & 48.6 & **77.4** & **56.0** & **67.3** & 49.4 & 70.6 & **54.0** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison between single (S.) and multiple (M.) event extraction on the second dataset. \begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**EF**} & \multicolumn{2}{c}{**EU**} & \multicolumn{2}{c}{**EO**} & \multicolumn{2}{c}{**EP**} & \multicolumn{2}{c}{**Average**} & \multicolumn{2}{c}{**Total**} \\  & **P** & **R** & **F1** & **P** & **R** & **F1** & **P** & **R** & **F1** & **P** & **R** & **F1** & **P** & **R** & **F1** & **P** & **R** & **F1** \\  & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** \\ \hline DEPPN [31] & 61.7 & **38.4** & 47.3 & 72.1 & 57.3 & 63.9 & 54.0 & 47.8 & 50.7 & 40.3 & 49.5 & 44.4 & 65.3 & 44.0 & 52.5 & 58.7 & 47.4 & 51.8 & 62.9 & 47.5 & 54.1 \\ GIT [32] & 69.2 & 33.2 & 44.9 & 69.1 & 61.3 & 65.0 & 61.4 & **52.8** & **56.7** & **70.0** & 55.5 & **62.0** & 68.2 & **59.0** & 63.3 & 67.6 & 52.4 & 58.4 & 68.2 & **57.4** & 62.3 \\ RAAT [34] & 70.0 & 36.3 & 47.8 & 55.0 & 50.1 & 52.4 & 57.7 & 43.4 & 49.6 & 51.5 & 49.7 & 50.6 & 62.3 & 57.7 & 59.9 & 59.3 & 47.5 & 52.1 & 59.7 & 53.2 & 56.3 \\ GNNDRS & **77.9** & 37.2 & **50.4** & **77.9** & **65.6** & **71.2** & **63.7** & 49.7 & 55.8 & 63.4 & **57.5** & 60.3 & **73.5** & 56.3 & **63.8** & **71.3** & **53.3** & **60.3** & 73.3 & 57.1 & **64.2** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Precision (P), recall (R), and F1 scores on the second dataset. \begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{1}{c}{**Model**} & \multicolumn{2}{c}{**S. (\%)**} & \multicolumn{2}{c}{**M. (\%)**} \\ \hline DEPPN [31] & 64.1 & 58.2 \\ GIT [32] & 80.2 & **61.1** \\ RAAT [34] & 69.2 & 54.5 \\ GNNDRS (ours) & **87.7** & 60.1 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of single (S.) and multiple (M.) event extraction on the first dataset. interactions between documents and sentences, the model can capture argument information across sentences and further understand the correlations between different pieces of information within an event. This global modeling approach helps improve the model's understanding of the overall semantics of events and enhances its ability to model relationships between different arguments. In addition, dynamically adapting the order of argument role detection endows the model to delve into contextual information within the document, enabling a more profound comprehension of the informational landscape. It also helps establish dependencies between multiple arguments, enhancing the semantic representation of events. These proposed strategies contribute to the superior performance of GNNDRS compared to the baselines.