  Based on an exponentially increasing number of academic articles, discovering
and citing comprehensive and appropriate resources has become a non-trivial
task. Conventional citation recommender methods suffer from severe information
loss. For example, they do not consider the section of the paper that the user
is writing and for which they need to find a citation, the relatedness between
the words in the local context (the text span that describes a citation), or
the importance on each word from the local context. These shortcomings make
such methods insufficient for recommending adequate citations to academic
manuscripts. In this study, we propose a novel embedding-based neural network
called "dual attention model for citation recommendation (DACR)" to recommend
citations during manuscript preparation. Our method adapts embedding of three
dimensions of semantic information: words in the local context, structural
contexts, and the section on which a user is working. A neural network is
designed to maximize the similarity between the embedding of the three input
(local context words, section and structural contexts) and the target citation
appearing in the context. The core of the neural network is composed of
self-attention and additive attention, where the former aims to capture the
relatedness between the contextual words and structural context, and the latter
aims to learn the importance of them. The experiments on real-world datasets
demonstrate the effectiveness of the proposed approach.
