  We present a novel model for the problem of ranking a collection of documents
according to their semantic similarity to a source (query) document. While the
problem of document-to-document similarity ranking has been studied, most
modern methods are limited to relatively short documents or rely on the
existence of "ground-truth" similarity labels. Yet, in most common real-world
cases, similarity ranking is an unsupervised problem as similarity labels are
unavailable. Moreover, an ideal model should not be restricted by documents'
length. Hence, we introduce SDR, a self-supervised method for document
similarity that can be applied to documents of arbitrary length. Importantly,
SDR can be effectively applied to extremely long documents, exceeding the 4,096
maximal token limits of Longformer. Extensive evaluations on large document
datasets show that SDR significantly outperforms its alternatives across all
metrics. To accelerate future research on unlabeled long document similarity
ranking, and as an additional contribution to the community, we herein publish
two human-annotated test sets of long documents similarity evaluation. The SDR
code and datasets are publicly available.
