  With the rapid growth of the scientific literature, manually selecting
appropriate citations for a paper is becoming increasingly challenging and
time-consuming. While several approaches for automated citation recommendation
have been proposed in the recent years, effective document representations for
citation recommendation are still elusive to a large extent. For this reason,
in this paper we propose a novel approach to citation recommendation which
leverages a deep sequential representation of the documents (Sentence-BERT)
cascaded with Siamese and triplet networks in a submodular scoring function. To
the best of our knowledge, this is the first approach to combine deep
representations and submodular selection for a task of citation recommendation.
Experiments have been carried out using a popular benchmark dataset - the ACL
Anthology Network corpus - and evaluated against baselines and a
state-of-the-art approach using metrics such as the MRR and F1-at-k score. The
results show that the proposed approach has been able to outperform all the
compared approaches in every measured metric.
