Abstract
Single document summarization has enjoyed
renewed interest 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