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SChuBERT: Scholarly Document Chunks with BERT-encoding boost Citation Count Prediction.
We also show the merit of using more training data and longer input for number of citations prediction.
MultiSChuBERT: Effective Multimodal Fusion for Scholarly Document Quality Prediction
Using BERT$_{\textrm{BASE}}$ embeddings, on the (log) number of citations prediction task with the ACL-BiblioMetry dataset, our MultiSChuBERT (text+visual) model obtains an $R^{2}$ score of 0. 454 compared to 0. 432 for the SChuBERT (text only) model.
SChuBERT: Scholarly Document Chunks with BERT-encoding boost Citation Count Prediction
We also show the merit of using more training data and longer input for number of citations prediction.
Structure-Tags Improve Text Classification for Scholarly Document Quality Prediction
To tackle these problems, we propose the use of HANs combined with structure-tags which mark the role of sentences in the document.
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