  Citing comprehensively and appropriately has become a challenging task with
the explosive growth of scientific publications. Current citation
recommendation systems aim to recommend a list of scientific papers for a given
text context or a draft paper. However, none of the existing work focuses on
already included citations of full papers, which are imperfect and still have
much room for improvement. In the scenario of peer reviewing, it is a common
phenomenon that submissions are identified as missing vital citations by
reviewers. This may lead to a negative impact on the credibility and validity
of the research presented. To help improve citations of full papers, we first
define a novel task of Recommending Missed Citations Identified by Reviewers
(RMC) and construct a corresponding expert-labeled dataset called CitationR. We
conduct an extensive evaluation of several state-of-the-art methods on
CitationR. Furthermore, we propose a new framework RMCNet with an Attentive
Reference Encoder module mining the relevance between papers, already-made
citations, and missed citations. Empirical results prove that RMC is
challenging, with the proposed architecture outperforming previous methods in
all metrics. We release our dataset and benchmark models to motivate future
research on this challenging new task.
