  Single document summarization has enjoyed renewed interests in recent years
thanks to the popularity of neural network models and the availability of
large-scale datasets. In this paper we develop an unsupervised approach arguing
that it is unrealistic to expect large-scale and high-quality training data to
be available or created for different types of summaries, domains, or
languages. We revisit a popular graph-based ranking algorithm and modify how
node (aka sentence) centrality is computed in two ways: (a)~we employ BERT, a
state-of-the-art neural representation learning model to better capture
sentential meaning and (b)~we build graphs with directed edges arguing that the
contribution of any two nodes to their respective centrality is influenced by
their relative position in a document. Experimental results on three news
summarization datasets representative of different languages and writing styles
show that our approach outperforms strong baselines by a wide margin.
