Leveraging multimodal content for podcast summarization

Vaiani, Lorenzo; Quatra, Moreno La; Cagliero, Luca; Garza, Paolo

doi:10.1145/3477314.3507106

Cited by 4 publications

(1 citation statement)

References 24 publications

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“…In recent years, ATS has received a lot of attention as it can be applied to a wide range of applications such as the extraction of highlights from scientific papers [2], the generation of summaries of news articles [3], and the creation of multimodal summaries of audio podcasts [4]. The summarization task can be also instrumental for other NLP tasks, e.g., by reducing the size of large documents to make them more suitable for downstream tasks [5].…”

Section: Introductionmentioning

confidence: 99%

BART-IT: An Efficient Sequence-to-Sequence Model for Italian Text Summarization

Quatra

Cagliero

2022

Future Internet

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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.

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