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
11/27/2023
cs.CL

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Abstract

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.

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Cite this paper

@article{tran2023overview,
  title  = {Overview of the VLSP 2022 -- Abmusu Shared Task: A Data Challenge for Vietnamese Abstractive Multi-document Summarization},
  author = {Mai-Vu Tran and Hoang-Quynh Le and Duy-Cat Can and Quoc-An Nguyen},
  year   = {2023},
  eprint = {2311.15525},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2311.15525v1}
}

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