Abstract
Graph-based approaches for multi-document summarization have been widely used to extract top sentences for a summary. Traditionally, the documents' cluster is modeled as a graph of the cluster's sentences only which might limit the ability of recognizing topically discriminative sentences in regard to other clusters. In this paper, we propose STARSUM a star bipartite graph which models sentences and their topic signature phrases. The approach ensures sentence similarity and content importance from the graph structure. We extract sentences in an approach that guarantees diversity and coverage which are crucial for multi-document summarization. Regardless of the simplicity of the approach in ranking, a DUC experiment shows the effectiveness of STARSUM compared to different baselines.