## 1 Introduction  
The purpose of the summarization process is to condense the most relevant information from a document or a set of documents into a small number of sentences. This process can be performed in an extractive or an abstractive way. While extractive summarization consists of identifying and copying those sentences in the original document that contain the most remarkable and useful information, abstractive summaries require abstractive actions that must be mastered. In this way, summaries are not mere clippings of the original documents; rather, abstractive summarizations are created by choosing the most important phrases of the documents and paraphrasing that content, creating a combination of some phrases, introducing new words, searching for synonyms, creating generalizations or specifications of some words or reordering content. All these actions must be done while preserving the linguistic cohesion and the coherence of the information [1, 2, 3, 4, 5]. Nowadays, Transformer-based language models excel in text generation, especially due to the transfer learning paradigm, by means of self-supervised pretraining on large text corpora, and later fine-tuning on downstream tasks. The generation capabilities achieved by these models boosted the state of the art in automatic summarization. However, most of the models proposed in the literature, such as BART [6], PEGASUS [7], or T5 [8] are intended to the English language and are not directly applicable to other languages. Multilingual models such as mBART [9] or mT5 [10] were also studied in the literature to address that language constraint, but despite their applicability being broader than that of the monolingual models, their performance is typically lower, especially on languages that are underrepresented in the pretraining corpora, or differ so much in linguistic terms from the most represented languages [11; 12; 13; 14]  
For minority languages like Catalan, the data resources available are much lower than other languages like English, Chinese, or Spanish. Additionally, the multilingual models typically either do not include data of minority languages, or if they do, its proportion in the pretraining sets is much lower than those of the majority languages. In this work, we hypothesize that monolingual models are a better choice for those minority languages, such as the Catalan language, which are underrepresented in the pretraining datasets of the multilingual models, but for which reasonable amounts of data are available. In this work, a BART-like summarization model for the Catalan language is pretrained from scratch, and then fine-tuned on the summarization task. During the pretraining step, we include several self-supervised tasks to enhance the of the degree of abstractivity of the generated summaries. Furthermore, to test our hypothesis about monolingual models, we compare the performance of our proposal against well-known pretrained multilingual models such as mBART and mT5. It is also interesting to study the performance of our proposal in languages with higher resources than Catalan. For this reason, we replicate the model and the experimentation for the Spanish language to extract conclusions about abstractivity and monolingual models in two different languages. We performed experimentation on the Dataset for Automatic summarization of Catalan and Spanish newspaper Articles (DACSA) corpus [15] This corpus provides pairs of news article and its summary from different journals in the Catalan and the Spanish languages. The experimental results show that the monolingual models generalize better than the multilingual ones, obtaining a more stable summarization performance on the test partitions of the DACSA dataset. The provided experimentation also illustrate the improvements in abstractivity as a result of the addition of the pretraining tasks. We analyze the abstractivity of the models through the use of abstractivity indicators [2].