## 4 Results
### DRG Accuracy  
Table 1 presents the text summarization accuracy. To each DRG model, the highest value of each metric is in boldface. It suggests that the optimal value of \(\beta\) in CLpCE is 0.60 regardless of decoding methods. On the other hand, no obvious difference is found among the highest metric values from DCS and CS decoding methods, and GS decoding achieves generally higher GOUGE and CIDER values.  
Table 1 indicates the superiority of the objective function CLpCE over CE or CL. When using DCS for decoding, CLpCE improves the report generation performance with \(\approx 0.03\) increases on BLEU and METEOR, 0.015 on ROUGE and 0.09 on CIDER metrics when \(\beta=0.6\). This phenomenon can also be found when using other decoding methods.  
Figure 3 shows the general trend of DRG accuracy when using different weighting values (\(\beta\in\{0.0,\cdots,1.0\}\)) and decoding methods (DCS, CS, GS, NS, and TkS). From the perspective of \(\beta\) values, compared to \(\beta=0.0\), the other \(\beta\) values lead to a slight increase (\(\leq 0.03\)) on metric values, except for \(\beta=1.0\). From the perspective of decoding methods, TkS and NS cause inferior results, and CIDER values are less than 1.10 and 1.35, respectively.  
The other decoding methods obtain slightly better performance, and the CIDER value from GS decoding is correspondingly higher.  
\begin{table}
\begin{tabular}{l l l l l l l l l} \hline \hline  & & \multicolumn{4}{c}{BLEU} & \multicolumn{2}{c}{METEOR} & \multicolumn{2}{c}{COIDER} \\ \cline{3-10}  & \(\beta\) & **BLEU-1** & **BLEU-2** & **BLEU-3** & **BLEU-4** & & & \\ \hline  & 0.0 (CE) & 0.4638 & 0.3838 & 0.3223 & 0.2724 & 0.2487 & 0.5057 & 1.3607 \\  & 0.1 & 0.4870 & 0.4049 & 0.3414 & 0.2893 & 0.2585 & 0.5170 & 1.4179 \\  & 0.2 & 0.4864 & 0.4047 & 0.3414 & 0.2893 & 0.2586 & 0.5186 & 1.4118 \\  & 0.3 & 0.4794 & 0.3987 & 0.3363 & 0.2853 & 0.2561 & 0.5154 & 1.4162 \\  & 0.4 & 0.4784 & 0.3979 & 0.3358 & 0.2851 & 0.2560 & 0.5179 & 1.4391 \\ DCS & 0.5 & 0.4805 & 0.3997 & 0.3372 & 0.2860 & 0.2564 & 0.5154 & 1.4147 \\  & 0.6 (CLpCE) & **0.4937** & **0.4107** & **0.3461** & **0.2933** & **0.2612** & 0.5182 & 1.4339 \\  & 0.7 & 0.4855 & 0.4040 & 0.3409 & 0.2894 & 0.2586 & **0.5199** & 1.4459 \\  & 0.8 & 0.4854 & 0.4033 & 0.3400 & 0.2884 & 0.2582 & 0.5195 & **1.4533** \\  & 0.9 & 0.4780 & 0.3968 & 0.3342 & 0.2834 & 0.2549 & 0.5147 & 1.4132 \\  & 1.0 (CL) & 0.0232 & 0.0013 & 0.0002 & 0.0000 & 0.0264 & 0.0284 & 0.0002 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Evaluation of CLpCE guided DRG models by using different decoding methods. The values highlighted in bold represent the highest scores for each metric, while the \(\beta\) value underlined indicates the objective function.  
Figure 3: The effect of different \(\beta\) values and decoding methods on DRG text summarization. In the plot, the horizontal axis denotes the \(\beta\) values in the CLpCE objective function, and the vertical axis presents the values of evaluation metrics. Specifically, combinations of different types of lines, markers and colors are used for identifying different metric values of a DRG model (BLEU-1, solid black line with \(\bigstar\); BLEU-2, dashed black line with \(\circ\); BLEU-3, dotted black line with \(\lozenge\); BLEU-4, dash-dotted black line with \(\square\); METEOR, dashed red line with \(\rhd\); ROUGE, dashed green line with \(\triangle\); and CIDER, dashed blue line with \(\bigtriangledown\)).