Dual Attention Model for Citation Recommendation

TL;DR

This paper introduces DACR, a neural network model with dual attention mechanisms that improves citation recommendation accuracy by considering local context, structural context, and paper section during manuscript writing.

Contribution

The paper presents a novel dual attention neural network that integrates multiple semantic dimensions for more accurate citation recommendations.

Findings

DACR outperforms existing methods on real-world datasets.

The dual attention mechanism effectively captures contextual relevance.

The model demonstrates significant improvement in citation accuracy.

Abstract

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.