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.

翻译：文档摘要是一项为单篇文档生成流畅且简洁摘要、并保留关键信息的任务。多文档摘要（MDS）以文档簇作为输入，并以簇摘要作为输出。本文聚焦于将抽取式MDS问题转化为子图选择问题。通过以图的形式处理问题，能够基于整体图结构和所选子图，同时捕捉同一文档内句子之间的关系以及同一簇内句子之间的关系。实验基于VLSP 2022评估竞赛发布的越南语数据集进行。该模型在公开测试集上以ROUGE-2的$F_1$指标计算，最终在所有参赛团队中排名前十。