Citation Recommendation Using Distributed Representation of Discourse Facets in Scientific Articles

Scientific articles usually follow a common pattern of discourse, and their contents can be divided into several facets, such as objective, method, and result. We examine the efficacy of using these discourse facets for citation recommendation. A method for learning multi-vector representations of scientific articles is proposed, in which each vector encodes a discourse facet present in an article. With each facet represented as a separate vector, the similarity of articles can be measured not in their entirety, but facet by facet. The proposed representation method is tested on a new citation recommendation task called context-based co-citation recommendation. This task calls for the evaluation of article similarity in terms of citation contexts, wherein facets help to abstract and generalize the diversity of contexts. The experimental results show that the facet-based representation outperforms the standard monolithic representation of articles.