  With the tremendous growth in the number of scientific papers being
published, searching for references while writing a scientific paper is a
time-consuming process. A technique that could add a reference citation at the
appropriate place in a sentence will be beneficial. In this perspective,
context-aware citation recommendation has been researched upon for around two
decades. Many researchers have utilized the text data called the context
sentence, which surrounds the citation tag, and the metadata of the target
paper to find the appropriate cited research. However, the lack of
well-organized benchmarking datasets and no model that can attain high
performance has made the research difficult.
  In this paper, we propose a deep learning based model and well-organized
dataset for context-aware paper citation recommendation. Our model comprises a
document encoder and a context encoder, which uses Graph Convolutional Networks
(GCN) layer and Bidirectional Encoder Representations from Transformers (BERT),
which is a pre-trained model of textual data. By modifying the related PeerRead
dataset, we propose a new dataset called FullTextPeerRead containing context
sentences to cited references and paper metadata. To the best of our knowledge,
This dataset is the first well-organized dataset for context-aware paper
recommendation. The results indicate that the proposed model with the proposed
datasets can attain state-of-the-art performance and achieve a more than 28%
improvement in mean average precision (MAP) and recall@k.
