Clustering sentences with density peaks for multi-document summarization

Abstract

Multi-document Summarization (MDS) is of great value to many real world applications. Many scoring models are proposed to select appropriate sentences from documents to form the summary, in which the clustering-based methods are popular. In this work, we propose a unified sentence scoring model which measures representativeness and diversity at the same time. Experimental results on DUC04 demonstrate that our MDS method outperforms the DUC04 best method and the existing clustering-based methods, and it yields close results compared to the state-of-the-art generic MDS methods. Advantages of the proposed MDS method are two-fold: (1) The density peaks clustering algorithm is firstly adopted, which is effective and fast. (2) No external resources such as Wordnet and Wikipedia or complex language parsing algorithms is used, making reproduction and deployment very easy in real environment.