Obtaining training data for multi-document summarization (MDS) is time
consuming and resource-intensive, so recent neural models can only be trained
for limited domains. In this paper, we propose SummPip: an unsupervised method
for multi-document summarization, in which we convert the original documents to
a sentence graph, taking both linguistic and deep representation into account,
then apply spectral clustering to obtain multiple clusters of sentences, and
finally compress each cluster to generate the final summary. Experiments on
Multi-News and DUC-2004 datasets show that our method is competitive to
previous unsupervised methods and is even comparable to the neural supervised
approaches. In addition, human evaluation shows our system produces consistent
and complete summaries compared to human written ones.

该研究提出了一种基于 unsupervised 方法的 SummPip，通过将原始文档转换成语句图，并考虑语言和深度表示，然后应用谱聚类来获得多个句子簇，并最终压缩每个簇以生成最终摘要。实验表明我们的方法可以与先前的无监督方法相竞争，并且甚至可以与神经监督方法相媲美。此外，人类评估显示我们的系统与人类编写的摘要相比是一致而完整的。