## 6 Results
### Abstractivity of the Summaries Generated by the Models for Catalan
\begin{table}
\begin{tabular}{l l l l l l l l} \hline \hline \multirow{2}{*}{**Partition**} & \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**Extractive**} & \multicolumn{1}{c}{**Content**} & \multicolumn{1}{c}{**Abstractivity\({}_{p}\)**} & \multicolumn{1}{c}{**Novel**} & \multicolumn{1}{c}{**Novel**} \\  & & **Fragment** & **Reedering** & **(\(\mathbf{p}\) = 2)** & \multicolumn{1}{c}{**1-grams**} & \multicolumn{1}{c}{**4-grams**} \\ \hline \multirow{3}{*}{TESTI} & NASCa & **96.99** (98.44, 97.04) & 46.17 (85.74, 86.75) & **47.19** (86.49, 7.44) & **03.21** (01.13, 02.26) & **28.65** (384.1, 28.02) \\  & mBART & 97.73 (97.64, 97.77) & **27.85** (97.44, 92.32) & 7.70 (97.62, 97.57) & (24.02, 03.04, 0.26) & 23.80 (53.54, 24.2) \\  & mT5 & 98.59 (98.58, 98.45) & 41.25 (98.44, 86.7) & 38.04 (97.28, 93.01) & (51.51 (98.4, 97.5) & 21.99 (91.71, 21.68) \\ \hline \multirow{3}{*}{TESTNI} & NASCa & **96.66** (95.5, 97.47) & 42.37 (41.54, 42.89) & **41.89** (91.44, 42.37) & **03.52** (00.44, 0.26) & **26.32** (25.54, 26.46) \\  & mBART & 97.08 (98.99, 98.16) & **42.96** (92.42, 03.45) & 36.98 (98.55, 37.41) & (30.01 (92.02, 0.09) & 24.32 (23.56, 24.70) \\  & mT5 & 98.31 (98.76, 98.30) & 38.82 (93.34, 93.41) & 39.18 (98.83, 93.74) & 01.80 (92.76, 98.29) & 23.02 (23.28, 24.48) \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Abstractivity indicators and confidence intervals for Catalan. Values are shown as percentages. this fact is also exposed by the _abstractivity\({}_{p}\)_ indicator, which presents a difference between the 5% and the 10% depending on the partition and the multilingual model. For all these observations in the indicators, we conclude that the NASca model generates summaries with higher degree of abstractivity than the multilingual models. With the aim of better analyzing the behavior of the models, we computed the cumulative distributions of the abstractivity indicators for each model and test partition. The results are presented in the Figure 1.  
The plots show in the x-axis the indicator measured, and in the y-axis, the percentage of generated summaries that present less or equal score to the value in the x-axis. These plots are helpful to evaluate the abstractivity of the generated summaries by taking into account how they are distributed based on certain score. If a metric correlates negatively with the abstractivity, it is desired that the scores be lower; that is, the model accumulates the samples fast. In contrast, if the metric correlates positively, it is desired that the scores be higher. In this case, we say that the model accumulates the samples slowly. In Figure 1, regarding the _coverage_ indicator, which correlates negatively with abstractivity, we observe that the NASca model stays always on top of the multilingual models, so this indicates that the samples are accumulated faster, which is a positive indication for the abstractivity. In the remaining indicators, which correlate positively with the abstractivity, the NASca model tends to accumulate the samples slower than the multilingual models, which is also positive concerning the abstractivity, except the _content reordering_ indicator. Regarding this indicator, although NASca present a lower value than the mBART model in the Section 6.2, the NASca model's distribution stays below the mBART until 40%, and later reaches and surpasses the multilingual models. This means that the _NACa_ model, overall, introduces less _content reordering_ on their summaries; however, the amount of summaries with rearrangement of the information is higher than in the ones generated by the multilingual models. The results presented in the Table 6 and the Figure 1 show enough evidences to conclude that the NASca model presents better abstractivity than the rest of the trained models. Additionally, to verify if the improvement in the abstractivity indicators is due to the pretraining tasks, we pretrained a BART model specifically for Catalan using only the pretraining tasks proposed in the original work [6].