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.

翻译：近些年来,由于文件简洁和数据独立,未经监督的文件摘要重新引起人们的极大关注。 在本文中,我们提出了一种基于图表的未经监督的采掘文件摘要化方法。我们的方法不是按显著和逐个抽取的句子排列顺序,而是通过使用图表中心和中子来进行汇总。我们首先根据句子图的中心点提取摘要候选人作为子集,然后通过匹配中子体从摘要候选人中选择。我们在两个有基准的汇总数据集上进行了广泛的实验,结果显示了我们模型相对于最新基线的有效性。