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Identifying the intent of a citation in scientific papers (e.g., backgroundinformation, use of methods, comparing results) is critical for machine readingof individual publications and automated analysis of the scientific literature.We propose structural scaffolds, a multitask model to incorporate structuralinformation of scientific papers into citations for effective classification ofcitation intents. Our model achieves a new state-of-the-art on an existing ACLanthology dataset (ACL-ARC) with a 13.3% absolute increase in F1 score, withoutrelying on external linguistic resources or hand-engineered features as done inexisting methods. In addition, we introduce a new dataset of citation intents(SciCite) which is more than five times larger and covers multiple scientificdomains compared with existing datasets. Our code and data are available at:https://github.com/allenai/scicite.

Structural Scaffolds for Citation Intent Classification in Scientific Publications