VLSP 2022 Abmusu Task Dataset: A Resource for Vietnamese Abstractive Multi-Document Summarization

The performance of automatic summarization systems has improved significantly with the development of supervised approaches. 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. We also used various summarization baselines to benchmark the Abmusu dataset.