VieSum: How Robust Are Transformer-based Models on Vietnamese Summarization?
This work addresses the limited research on summarization for Vietnamese text, which is an incremental contribution to domain-specific NLP.
The paper investigated the robustness of transformer-based encoder-decoder models for Vietnamese abstractive summarization, validating their performance on two Vietnamese datasets using transfer learning and self-supervised learning.
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.