## 3 Newspapers Summarization Corpus  
As stated above, the models proposed in this work are focused on the specific domain of newspaper articles. To train the models, the Dataset for Automatic summarization of Catalan and Spanish newspaper Articles (DACSA) [15] corpus was used. This corpus provides pairs of news article and its summary from different newspapers for both, the Catalan and the Spanish languages.  
Regarding the Catalan set, there are 725,184 sample pairs from 9 newspapers, and their distribution is shown in the Table 1:  
Regarding the Spanish set, the corpus provides 2,120,649 sample pairs from 21 newspapers, distributed as it is detailed in the Table 2:  
When the distributions of the samples on both subsets are analyzed, the amount of samples by source is far from being homogeneous. If these distributions preserve over the partitions (training, validation, and test set), the models will focus their learning on the newspapers that are predominant. To avoid this bias and achieve more general models, the test and validation sets were created in a way that ensured that all newspapers had roughly the same number of samples on those sets. To achieve this balance in the validation and test sets, the sources with less samples were discarded. In this way, it is guaranteed that all sources represent at least 5% of samples in each one of these two sets. The sources that were excluded are marked with an asterisk in the Tables 1 and 2.  
The three sets for Catalan include 6 of the 9 newspapers, creating a training set that contains 636,596 samples and 35,376 samples for validation and test sets. In the case of Spanish, the three sets are composed of 13 of the 21 newspapers provided in the Spanish set of DACSA: the training set contains 1,802,919 samples, and the validation and test sets contain 104,052 samples each.  
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline  & & & **Article** & & & **Summary** \\ \hline
**Source** & **Docs** & **Tokens** & [V] & \begin{tabular}{c} **Sents** \\ **Per Doc** \\ \end{tabular} & \begin{tabular}{c} **Words** \\ **Per Sent** \\ \end{tabular} & \
\begin{tabular}{c} **Sents** \\ **Per Doc** \\ \end{tabular} &
\begin{tabular}{c} **Words** \\ **Per Sent** \\ \end{tabular} \\ \hline
#1 & 238,233 & 114,500,016 & 614,146 & 17.68 & 27.19 & 115,954 & 1.14 & 20.16 \\
#2 & 194,697 & 105,119,526 & 621,612 & 19.99 & 27.01 & 112,904 & 1.28 & 19.14 \\
#3 & 137,447 & 63,683,416 & 485,286 & 14.99 & 30.92 & 91,975 & 1.05 & 22.65 \\
#4 & 56,827 & 24,891,291 & 276,720 & 14.84 & 29.52 & 58,071 & 1.21 & 17.52 \\
#5 & 44,381 & 26,977,332 & 277,225 & 18.04 & 33.69 & 55,216 & 1.15 & 23.86 \\
#6 & 35,763 & 17,181,460 & 202,931 & 11.31 & 42.49 & 42.289 & 1.05 & 22.79 \\
#7 * & 7104 & 3,800,842 & 83,942 & 18.04 & 29.66 & 19,267 & 1.02 & 26.51 \\
#8 * & 5882 & 9,414,192 & 185,977 & 66.04 & 24.24 & 31,006 & 2.54 & 24.84 \\
#9 * & 4850 & 2,667,185 & 102,024 & 23.61 & 23.29 & 19,584 & 1.16 & 28.05 \\ \hline Set & 725,184 & 368,235,260 & 1,326,343 & 17.71 & 28.67 & 223,978 & 1.17 & 20.59 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Statistics of Catalan set. Sources marked with * were not used for training the models.  
All the sources excluded were used as a separate test set. This partition allows to evaluate the generalization capabilities of the models. In this work, we refer to the test set with newspapers included in the training set as TEST1 and to the test set that contains newspapers not included in the training set as TESTni. The statistics of all the sets are shown in Tables 3 and 4.