Citation Intent Classification and Its Supporting Evidence Extraction for Citation Graph Construction

As the significant growth of scientific publications in recent years, an efficient way to extract scholarly knowledge and organize the relationship among literature is necessitated. Previous works constructed scientific knowledge graph with authors, papers, citations, and scientific entities. To assist researchers to grasp the research context comprehensively, this paper constructs a fine-grained citation graph in which citation intents and their supporting evidence are labeled between citing and cited papers instead. We propose a model with a Transformer encoder to encode the long-lengthy paper. To capture the coreference relations of words and sentences in a paper, a coreference graph is created by utilizing Gated Graph Convolution Network (GGCN). We further propose a graph modification mechanism to dynamically update the coreference links. Experimental results show that our model achieves promising results on identifying multiple citation intents in sentences.