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With the tremendous growth in the number of scientific papers beingpublished, searching for references while writing a scientific paper is atime-consuming process. A technique that could add a reference citation at theappropriate place in a sentence will be beneficial. In this perspective,context-aware citation recommendation has been researched upon for around twodecades. Many researchers have utilized the text data called the contextsentence, which surrounds the citation tag, and the metadata of the targetpaper to find the appropriate cited research. However, the lack ofwell-organized benchmarking datasets and no model that can attain highperformance has made the research difficult.  In this paper, we propose a deep learning based model and well-organizeddataset for context-aware paper citation recommendation. Our model comprises adocument 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 PeerReaddataset, we propose a new dataset called FullTextPeerRead containing contextsentences to cited references and paper metadata. To the best of our knowledge,This dataset is the first well-organized dataset for context-aware paperrecommendation. The results indicate that the proposed model with the proposeddatasets can attain state-of-the-art performance and achieve a more than 28%improvement in mean average precision (MAP) and recall@k.

A Context-Aware Citation Recommendation Model with BERT and Graph Convolutional Networks