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However, in the Vietnamese abstractive multi-document summarization task, the available datasets are insufficient for training the model. With this motivation, we contribute a new gold standard Vietnamese abstractive multi-document summarization dataset, named Abmusu. Following the collecting and clustering of articles, we have built a hierarchical annotation process to generate summaries, with three roles: annotator, supervisor, and curator. As a result, the dataset contains 600 news clusters formed from 1839 articles and the corresponding human-generated summaries. To the best of our knowledge, Abmusu dataset is the biggest dataset for Vietnamese abstractive multi-document summarization that is freely available for research. Moreover, summaries are more concise, making it challenging to train the summarization models. 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