## 6 Results
### Summarization Performance and Abstractivity of the Summaries Generated by the Models for Spanish  
It is also interesting to study the performance of our proposal in languages with higher resources than Catalan. For this reason, we replicated the model and the experimentation for the Spanish language. The summarization performance results and the results related to the abstractivity indicators are shown in Tables 7 and 8, respectively. In addition, the cumulative distributions of the abstractivity indicators are presented in Figure 2.  
Table 7 shows that the NASes model presents the best performance of the three models in the TESTi partition. All the scores obtained by the NASes model are significantly better compared to those of the multilingual models. Specifically, the NASes model achieve, on average, 8.2% higher performance than mBART and 4.5% higher than mT5. Regarding the TESTni partition, the NASes model reduces its performance in average, while mT5 achieves the best results in almost all the metrics. The results show that the NASes excelled in the TESTi partition, which contains newspapers included in the training partition. However, NASes presents lower generalization capabilities than the multilingual models due to the noticeable performance reduction in the TESTni partition, which contains newspapers not included in the training partition. Regarding the abstractivity indicators on the TESTi partition, presented in Table 8, all the scores of the NASes model are significantly better than those of the multilingual models. In the TESTni partition, the models present less abstractivity in comparison to the TESTi partition. Also in TESTni, the NASes model shows significant differences compared to the multilingual models in all the indicators, excluding \(\mathit{abstractivity}_{p}\) where mBART obtains better scores than NASes and the mT5 models. We also computed the cumulative distributions of the abstractivity indicators for each model and test partition. The results are presented in the Figure 2. \begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Partition** & **Model** & **ROUGE-1** & **ROUGE-2** & **ROUGE-L** & **ROUGE-Ls** & **BERTScore** \\ \hline \multirow{3}{*}{TESTTi} & NASes & **33.24**(3.12, 33.38) & **15.79**(15.84, 15.80) & **26.76**(26.84, 26.89) & **27.56**(27.43, 27.69) & **73.11**(73.76, 73.16) \\  & mBART & 31.09 (30.96, 31.20) & 13.56 (33.44, 13.68) & 24.67 (24.56, 26.78) & 25.48 (25.37, 25.58) & 72.25 (72.12, 72.30) \\  & mT5 & 31.72 (31.80, 31.85) & 14.54 (4.39, 14.67) & 25.76 (25.82, 25.80) & 26.31 (6.18, 28.44) & 72.86 (22.82, 27.51) \\ \hline \multirow{3}{*}{TESTNi} & NASes & 30.60 (0.92, 30.66) & 10.75 (0.06, 10.83) & 22.29 (231, 22.37) & 23.06 (0.92, 29.23) & 70.66 (0.06, 70.69) \\  & mBART & **30.66**(0.95, 30.74) & 12.08 (0.18, 12.18) & 23.13 (23.26, 23.22) & 23.89 (23.81, 23.98) & 10.77 (0.16, 74.10) \\ \cline{1-1}  & mT5 & 30.61 (0.95, 30.70) & **12.36**(12.25, 12.47) & **23.53**(23.24, 23.63) & **24.05**(0.95, 30.54, 31.46) & **71.26**(0.12, 71.30) \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Average F1 scores and confidence intervals of models in summarization task in Spanish.