Overview of the VLSP 2022 -- Abmusu Shared Task: A Data Challenge for
  Vietnamese Abstractive Multi-document Summarization
Mai-Vu Tran Hoang-Quynh Le Duy-Cat Can Quoc-An Nguyen
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.