Integrating Title and Citation Context Semantics of Citing Paper via Weighted Attentions for Local Citation Recommendation

Abstract

Citing comprehensively and appropriately has become a challenging task with the rapid growth of academic publications. Citation recommendation helps alleviate the burden of finding relevant and appropriate citations by recommending a list of academic papers for a given text. Existing approaches tried to determine whether a paper should be cited by measuring its relevance or similarity to a given citation circumstance. Citation circumstance modeling should consider many factors, such as the content of the citing paper, citation context words, citation network, and so on. However, most models only focus on citation context words and learn representations of citing paper through citation networks, putting little attention on citing paper content. In this paper, we regard the title as a summative and informative sentence relative to paper content and propose a novel model based on Bidirectional Gated Recurrent Units (BiGRUs) and Attentions. Our approach uses sequential embedding of paper title words for paper semantic representation and models citation circumstances by integrating citing paper title and citation context. Moreover, we introduce a semantic weight parameter to distinguish the importance of academic papers and citation contexts. Experiments on the ACL anthology network dataset show that our approach outperforms other state-of-the-art models in recall, MAP, MRR, and nDCG criteria.