  Unsupervised extractive summarization aims to extract salient sentences from
a document as the summary without labeled data. Recent literatures mostly
research how to leverage sentence similarity to rank sentences in the order of
salience. However, sentence similarity estimation using pre-trained language
models mostly takes little account of document-level information and has a weak
correlation with sentence salience ranking. In this paper, we proposed two
novel strategies to improve sentence similarity estimation for unsupervised
extractive summarization. We use contrastive learning to optimize a
document-level objective that sentences from the same document are more similar
than those from different documents. Moreover, we use mutual learning to
enhance the relationship between sentence similarity estimation and sentence
salience ranking, where an extra signal amplifier is used to refine the pivotal
information. Experimental results demonstrate the effectiveness of our
strategies.
