The multi-document summarization task requires the designed summarizer to
generate a short text that covers the important information of original
documents and satisfies content diversity. This paper proposes a multi-document
summarization approach based on hierarchical clustering of documents. It
utilizes the constructed class tree of documents to extract both the sentences
reflecting the commonality of all documents and the sentences reflecting the
specificity of some subclasses of these documents for generating a summary, so
as to satisfy the coverage and diversity requirements of multi-document
summarization. Comparative experiments with different variant approaches on
DUC'2002-2004 datasets prove the effectiveness of mining both the commonality
and specificity of documents for multi-document summarization. Experiments on
DUC'2004 and Multi-News datasets show that our approach achieves competitive
performance compared to the state-of-the-art unsupervised and supervised
approaches.

本文提出了一种基于文档层次聚类的多文档自动摘要方法，通过提取反映所有文档共性和部分子类特异性的句子生成摘要，从而满足多文档摘要的覆盖和多样性要求。在 DUC'2002-2004 和 Multi-News 数据集上的实验证明了该方法的有效性。