## 2 Related Work  
Abstractive summarization works normally focused on the creation of models using approaches different to those used for extractive summarization [17; 18; 19; 20; 21; 22]. Recently, abstractive summarizers became ubiquitous due to their powerful generation capabilities, achieved by using encoder-decoder architectures with Transformers [23] as backbone, and by pretraining them with self-supervised language modeling tasks on massive text corpora. This kind of models, especially PEGASUS [7], BART [6], T5 [8] and ProphetNet [24], fine-tuned for summarization tasks, are the state of the art in abstractive summarization benchmarks. While all these models are nearly identical regarding their architecture, they mainly differ in the self-supervised tasks used in the pretraining stage. In some cases, such as BART, T5, and ProphetNet, these tasks aims the models to learn general aspects of the language, e.g., by masking tokens or reordering sentences. More specifically, BART is pretrained to reconstruct masked spans (text infilling) and to arrange sentences in the original order after being permuted (sentence permutation). Similarly, T5 is pretrained on encoder-decoder masked language modeling, in order to address universally all text-based language problems in a text-to-text format. Regarding ProphetNet, it is pretrained on future n-gram prediction to encourage the model to plan for future tokens instead of the next token, which prevents overfitting on strong local correlations. However, in other cases such as PEGASUS, the self-supervised tasks intentionally resemble the summarization task to encourage whole-document understanding and summary-like generation. In contrast to the previous models, PEGASUS is trained with Gap Sentences Generation (GSG), which consists of reconstructing the sentences that maximize the ROUGE with respect to the whole document. In this way, the authors of PEGASUS hypothesize that GSG is more suitable for abstractive summarization than other pretraining strategies, as it closely resembles the downstream task. Other works are also based on strategies that involve pretraining to improve the abstractivity of the generated summaries. For instance, in [25], domain transfer and data synthesis techniques by using pretrained models are explored to improve the performance of abstractive summarization models in low-resource scenarios. Also, the authors of [26] propose to use pretrained language models to incorporate prior knowledge about language generation, which provides results comparable to state-of-the-art models in terms of ROUGE, while increasing the level of abstraction of the generated summaries, measured in terms of n-gram overlapping. Finally, in [27] a combination of several pretraining tasks is introduced to tailor the models to abstractive summarization, improving performance upon other Transformer-based models with significantly less pretraining data. Specifically, three tasks were proposed for pretraining: sentence reordering, next segment generation and masked document generation. While sentence reordering and masked document generation are identical to the text infilling and sentence permutation tasks used in BART, next segment generation aims to complete a document given a prefix of that document. Therefore, our work is similar to [27] in the sense that we combine the pretraining tasks of BART and PEGASUS to improve the abstractive skills of monolingual models trained for Catalan and Spanish. All the models and proposals discussed in this section are intended for the English language, however, there are many other languages that deserve attention. Some efforts were done to consider other languages along with the English language by means of multilingual models such as mBART [9] or mT5 [10]. Although these efforts are very convenient and useful in many cases, the performance of the multilingual models is typically lower on languages that are underrepresented in the pretraining data or differ so much, in linguistic terms, from the most represented languages [13; 14].