Structural Scaffolds for Citation Intent Classification in Scientific Publications
Table 3: Results on the ACL-ARC citations dataset.
['[EMPTY]', 'Model', 'macro F1']
Our main results for the ACL-ARC dataset Jurgens et al. We observe that our scaffold-enhanced models achieve clear improvements over the state-of-the-art approach on this task. Starting with the ‘BiLSTM-Attn’ baseline with a macro F1 score of 51.8, adding the first scaffold task in ‘BiLSTM-Attn + section title scaffold’ improves the F1 score to 56.9 (Δ=5.1). Adding the second scaffold in ‘BiLSTM-Attn + citation worthiness scaffold’ also results in similar improvements: 56.3 (Δ=4.5). When both scaffolds are used simultaneously in ‘BiLSTM-Attn + both scaffolds’, the F1 score further improves to 63.1 (Δ=11.3), suggesting that the two tasks provide complementary signal that is useful for citation intent prediction.