A Survey of Citation Recommendation Tasks and Methods

Abstract

Scientific articles store vast amounts of knowledge amassed through many decades of research. They serve to communicate research results among scientists but also for learning and tracking progress in the field. However, scientific production has risen to levels that make it difficult even for experts to keep up with work in their field. As a remedy, specialized search engines are being deployed, incorporating novel natural language processing and machine learning methods. The task of citation recommendation, in particular, has attracted much interest as it holds promise for improving the quality of scientific production. In this paper, we present the state-of-the-art in citation recommendation: we survey the methods for global and local approaches to the task, the evaluation setups and datasets, and the most successful machine learning models. In addition, we overview two tasks complementary to citation recommendation: extraction of key aspects and entities from articles and citation function classification. With this survey, we hope to provide the ground for understanding current efforts and stimulate further research in this exciting and promising field.