  Unsupervised document summarization has re-acquired lots of attention in
recent years thanks to its simplicity and data independence. In this paper, we
propose a graph-based unsupervised approach for extractive document
summarization. Instead of ranking sentences by salience and extracting
sentences one by one, our approach works at a summary-level by utilizing graph
centrality and centroid. We first extract summary candidates as subgraphs based
on centrality from the sentence graph and then select from the summary
candidates by matching to the centroid. We perform extensive experiments on two
bench-marked summarization datasets, and the results demonstrate the
effectiveness of our model compared to state-of-the-art baselines.
