mlsanityData to 14 Oct 2026

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

arXiv 2110.04257 ↗First posted 8 Oct 2021cs.CLcited by 1

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Abstract (arXiv)

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.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 22 of its 25 references that are in the map. First posted on arXiv 8 Oct 2021.

Builds on

22 of 25 references.

References · 22

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10 more
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Citing papers per year: 2022: 1.

1 paper.

  1. 2022ViT5: Pretrained Text-to-Text Transformer for Vietnamese Language Generationcited by 37