Structural Scaffolds for Citation Intent Classification in Scientific Publications

Identifying the intent of a citation in scientific papers (e.g., background information, use of methods, comparing results) is critical for machine reading of individual publications and automated analysis of the scientific literature. We propose structural scaffolds, a multitask model to incorporate structural information of scientific papers into citations for effective classification of citation intents. Our model achieves a new state-of-the-art on an existing ACL anthology dataset (ACL-ARC) with a 13.3% absolute increase in F1 score, without relying on external linguistic resources or hand-engineered features as done in existing methods. In addition, we introduce a new dataset of citation intents (SciCite) which is more than five times larger and covers multiple scientific domains compared with existing datasets. Our code and data are available at: https://github.com/allenai/scicite.

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Datasets


Introduced in the Paper:

SciCite

Used in the Paper:

ACL ARC

Results from the Paper


 Ranked #1 on Citation Intent Classification on ACL-ARC (using extra training data)

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Task Dataset Model Metric Name Metric Value Global Rank Uses Extra
Training Data
Result Benchmark
Citation Intent Classification ACL-ARC Structural-scaffolds F1 67.9 # 1
Sentence Classification ACL-ARC Structural-scaffolds F1 67.9 # 3
Citation Intent Classification SciCite Structural-Scaffolds F1 84.0 # 2
Sentence Classification SciCite Structural-scaffolds F1 84 # 3

Methods


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