  We present RepRank, an unsupervised graph-based ranking model for extractive
multi-document summarization in which the similarity between words, sentences,
and word-to-sentence can be estimated by the distances between their vector
representations in a unified vector space. In order to obtain desirable
representations, we propose a self-attention based learning method that
represent a sentence by the weighted sum of its word embeddings, and the
weights are concentrated to those words hopefully better reflecting the content
of a document. We show that salient sentences and keywords can be extracted in
a joint and mutual reinforcement process using our learned representations, and
prove that this process always converges to a unique solution leading to
improvement in performance. A variant of absorbing random walk and the
corresponding sampling-based algorithm are also described to avoid redundancy
and increase diversity in the summaries. Experiment results with multiple
benchmark datasets show that RepRank achieved the best or comparable
performance in ROUGE.
