Multi-document summarization is challenging because the summaries should not only describe the most important information from all documents but also provide a coherent interpretation of the documents. This paper proposes a method for multi-document summarization based on cluster similarity. In the extractive method we use hybrid model based on a modified version of the PageRank algorithm and a text correlation considerations mechanism. After generating summaries by selecting the most important sentences from each cluster, we apply BARTpho and ViT5 to construct the abstractive models. Both extractive and abstractive approaches were considered in this study. The proposed method achieves competitive results in VLSP 2022 competition.

翻译：多文档摘要具有挑战性，因为摘要不仅要描述所有文档中最重要的信息，还需提供对文档的连贯解释。本文提出了一种基于聚类相似度的多文档摘要方法。在抽取式方法中，我们采用基于改进版PageRank算法与文本相关性考量机制的混合模型。通过从每个聚类中选择最重要的句子生成摘要后，我们应用BARTpho和ViT5构建生成式模型。本研究同时考虑了抽取式与生成式两种方法。所提出的方法在VLSP 2022竞赛中取得了具有竞争力的结果。