  Unsupervised approaches to extractive summarization usually rely on a notion
of sentence importance defined by the semantic similarity between a sentence
and the document. We propose new metrics of relevance and redundancy using
pointwise mutual information (PMI) between sentences, which can be easily
computed by a pre-trained language model. Intuitively, a relevant sentence
allows readers to infer the document content (high PMI with the document), and
a redundant sentence can be inferred from the summary (high PMI with the
summary). We then develop a greedy sentence selection algorithm to maximize
relevance and minimize redundancy of extracted sentences. We show that our
method outperforms similarity-based methods on datasets in a range of domains
including news, medical journal articles, and personal anecdotes.
