This paper reports the overview of the VLSP 2022 - Vietnamese abstractive
multi-document summarization (Abmusu) shared task for Vietnamese News. This
task is hosted at the 9$^{th}$ annual workshop on Vietnamese Language and
Speech Processing (VLSP 2022). The goal of Abmusu shared task is to develop
summarization systems that could create abstractive summaries automatically for
a set of documents on a topic. The model input is multiple news documents on
the same topic, and the corresponding output is a related abstractive summary.
In the scope of Abmusu shared task, we only focus on Vietnamese news
summarization and build a human-annotated dataset of 1,839 documents in 600
clusters, collected from Vietnamese news in 8 categories. Participated models
are evaluated and ranked in terms of \texttt{ROUGE2-F1} score, the most typical
evaluation metric for document summarization problem.

本论文报告了越南语新闻的 VLSP 2022 - 越南摘要多文档自动化生成（Abmusu）共享任务的概述。该任务旨在开发能够自动为一组关于某个主题的文档创建摘要的自动化系统。参与的模型根据 ROUGE2-F1 分数进行评估和排名。

VLSP 2022 概述 --Abmusu 共享任务：越南摘要多文档概括性总结的数据挑战

Overview of the VLSP 2022 -- Abmusu Shared Task: A Data Challenge for  Vietnamese Abstractive Multi-document Summarization

We present our systems participated in the VLSP 2022 machine translation
shared task. In the shared task this year, we participated in both translation
tasks, i.e., Chinese-Vietnamese and Vietnamese-Chinese translations. We build
our systems based on the neural-based Transformer model with the powerful
multilingual denoising pre-trained model mBART. The systems are enhanced by a
sampling method for backtranslation, which leverage large scale available
monolingual data. Additionally, several other methods are applied to improve
the translation quality including ensembling and postprocessing. We achieve
38.9 BLEU on ChineseVietnamese and 38.0 BLEU on VietnameseChinese on the public
test sets, which outperform several strong baselines.

我们的研究使用了基于神经网络的 Transformer 模型和 mBART 预训练模型，在 VLSP 2022 机器翻译任务中取得了 38.9 BLEU 的中越翻译和 38.0 BLEU 的越中翻译结果，超过了多个强基准。