Abstract
Most of the models proposed in the literature for abstractive summarization are generally suitable for the English language but not for other languages. Multilingual models were introduced to address that language constraint, but despite their applicability being broader than that of the monolingual models, their performance is typically lower, especially for minority languages like Catalan. In this paper, we present a monolingual model for abstractive summarization of textual content in the Catalan language. The model is a Transformer encoder-decoder which is pretrained and fine-tuned specifically for the Catalan language using a corpus of newspaper articles. In the pretraining phase, we introduced several self-supervised tasks to specialize the model on the summarization task and to increase the abstractivity of the generated summaries. To study the performance of our proposal in languages with higher resources than Catalan, we replicate the model and the experimentation for the Spanish language. The usual evaluation metrics, not only the most used ROUGE measure but also other more semantic ones such as BertScore, do not allow to correctly evaluate the abstractivity of the generated summaries. In this work, we also present a new metric, called content reordering, to evaluate one of the most common characteristics of abstractive summaries, the rearrangement of the original content. We carried out an exhaustive experimentation to compare the performance of the monolingual models proposed in this work with two of the most widely used multilingual models in text summarization, mBART and mT5. The experimentation results support the quality of our monolingual models, especially considering that the multilingual models were pretrained with many more resources than those used in our models. Likewise, it is shown that the pretraining tasks helped to increase the degree of abstractivity of the generated summaries. To our knowledge, this is the first work that explores a monolingual approach for abstractive summarization both in Catalan and Spanish.
Highlights
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
Multilingual models such as mBART [9] or mT5 [10] were 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]
Monolingual pretraining in languages other than English is still unexplored for language generation tasks such as abstractive summarization. This is the first work that explores a monolingual approach for abstractive summarization both in Catalan and Spanish
Summary
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. 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. A monolingual abstractive text summarization model, News Abstract Summarization for Catalan (NASCA), is proposed This model, based on the BART architecture [6], is pretrained with several self-supervised tasks to improve the abstractivity of the generated summaries. The monolingual models, NASCA (https://huggingface.co/ELiRF/NASCA, accessed on 19 October 2021) and NASES (https://huggingface.co/ELiRF/NASES, accessed on 19 October 2021), proposed in this work were publicly release through HuggingFace model hub [16]
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