  Document summarization is a task to generate afluent, condensed summary for a
document, andkeep important information. A cluster of documents serves as the
input for multi-document summarizing (MDS), while the cluster summary serves as
the output. In this paper, we focus on transforming the extractive MDS problem
into subgraph selection. Approaching the problem in the form of graphs helps to
capture simultaneously the relationship between sentences in the same document
and between sentences in the same cluster based on exploiting the overall graph
structure and selected subgraphs. Experiments have been implemented on the
Vietnamese dataset published in VLSP Evaluation Campaign 2022. This model
currently results in the top 10 participating teams reported on the ROUGH-2
$F\_1$ measure on the public test set.
