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 on which a user is working , the relatedness between words , or the importance of words . These shortcomings make such methods insufficient for recommending adequate citations when working on manuscripts. In this study, we propose a novel approach called dual attention model for citation recommendation (DACR)  to recommend citations during manuscript preparation. Our method considers three dimensions of information: contextual words , structural contexts, and the section on which a user is working.  The core of the proposed model is composed of self-attention and additive attention, where the former aims to capture the relatedness between input information , and the latter aims to learn the importance of inputs . The experiments on real-world datasets demonstrate the effectiveness of the proposed approach.
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.
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on which a user is working
of the paper that the user is writing and for which they need to find a citation
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words
the words in the local context (the text span that describes a citation)
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of words
on each word from the local context
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when working on
to academic
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approach called
embedding-based neural network called "
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"
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considers
adapts embedding of
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information: contextual words
semantic information: words in the local context
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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.
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proposed model
neural network
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input information
the contextual words and structural context
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inputs
them