BART-IT: An Efficient Sequence-to-Sequence Model for Italian Text Summarization
The emergence of attention-based architectures has led to significant improvements in the performance of neural sequence-to-sequence models for text summarization. Although these models have proved to be effective in summarizing English-written documents, their portability to other languages is limited thus leaving plenty of room for improvement. In this paper, we present BART-IT, a sequence-to-sequence model, based on the BART architecture that is specifically tailored to the Italian language. The model is pre-trained on a large corpus of Italian-written pieces of text to learn language-specific features and then fine-tuned on several benchmark datasets established for abstractive summarization. The experimental results show that BART-IT outperforms other state-of-the-art models in terms of ROUGE scores in spite of a significantly smaller number of parameters. The use of BART-IT can foster the development of interesting NLP applications for the Italian language. Beyond releasing the model to the research community to foster further research and applications, we also discuss the ethical implications behind the use of abstractive summarization models.
Code (1)
Tasks
Abstractive Text SummarizationText SummarizationMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
AraBART: a Pretrained Arabic Sequence-to-Sequence Model for Abstractive Summarization
Like most natural language understanding and generation tasks, state-of-the-art models for summarization are transformer-based sequence-to-sequence architectures that are pretrained on large corpora. While most existing …
Abstractive Text SummarizationDecoderNatural Language UnderstandingAraBART: a Pretrained Arabic Sequence-to-Sequence Model for Abstractive Summarization
Like most natural language understanding and generation tasks, state-of-the-art models for summarization are transformer-based sequence-to-sequence architectures that are pretrained on large corpora. While most existing …
Abstractive Text SummarizationDecoderNatural Language UnderstandingBARTpho: Pre-trained Sequence-to-Sequence Models for Vietnamese
We present BARTpho with two versions, BARTpho-syllable and BARTpho-word, which are the first public large-scale monolingual sequence-to-sequence models pre-trained for Vietnamese. BARTpho uses the "large" architecture an…
Abstractive Text SummarizationDenoisingPunctuation RestorationText SummarizationTwo New Datasets for Italian-Language Abstractive Text Summarization
Text summarization aims to produce a short summary containing relevant parts from a given text. Due to the lack of data for abstractive summarization on low-resource languages such as Italian, we propose two new original…
Abstractive Text SummarizationArticlesMachine TranslationSentence+3IndicBART: A Pre-trained Model for Indic Natural Language Generation
We study pre-trained sequence-to-sequence model for a specific-language family with a focus on Indic languages. We present IndicBART, a multilingual, sequence-to-sequence pre-trained model focusing on 11 Indic languages…
Extreme SummarizationMachine TranslationNMTText Generation+2