VieSum: How Robust Are Transformer-based Models on Vietnamese Summarization?

• URL: http://arxiv.org/abs/2110.04257v1
• Date: Fri, 8 Oct 2021 17:10:31 GMT
• Title: VieSum: How Robust Are Transformer-based Models on Vietnamese Summarization?
• Authors: Hieu Nguyen, Long Phan, James Anibal, Alec Peltekian, Hieu Tran
• Abstract summary: We investigate the robustness of transformer-based encoder-decoder architectures for Vietnamese abstractive summarization. We validate the performance of the methods on two Vietnamese datasets.
• Score: 1.1379578593538398
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Text summarization is a challenging task within natural language processing that involves text generation from lengthy input sequences. While this task has been widely studied in English, there is very limited research on summarization for Vietnamese text. In this paper, we investigate the robustness of transformer-based encoder-decoder architectures for Vietnamese abstractive summarization. Leveraging transfer learning and self-supervised learning, we validate the performance of the methods on two Vietnamese datasets.

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