SChuBERT: Scholarly Document Chunks with BERT-encoding boost Citation Count Prediction
This work provides a more accurate method for predicting citation counts, which can serve as a quality proxy for researchers and institutions.
This paper addresses the problem of predicting scholarly document citation counts by creating a large corpus from the ACL Anthology and Semantic Scholar. Their proposed model, SChuBERT, significantly outperforms existing state-of-the-art methods.
Predicting the number of citations of scholarly documents is an upcoming task in scholarly document processing. Besides the intrinsic merit of this information, it also has a wider use as an imperfect proxy for quality which has the advantage of being cheaply available for large volumes of scholarly documents. Previous work has dealt with number of citations prediction with relatively small training data sets, or larger datasets but with short, incomplete input text. In this work we leverage the open access ACL Anthology collection in combination with the Semantic Scholar bibliometric database to create a large corpus of scholarly documents with associated citation information and we propose a new citation prediction model called SChuBERT. In our experiments we compare SChuBERT with several state-of-the-art citation prediction models and show that it outperforms previous methods by a large margin. We also show the merit of using more training data and longer input for number of citations prediction.