Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: SChuBERT: Scholarly Document Chunks with BERT-encoding boost Citation
  Count Prediction   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.

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The categories of this paper are: stat
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The categories of this paper are: funct-an
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The categories of this paper are: supr-con
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The categories of this paper are: math
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The categories of this paper are: hep-ex
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The categories of this paper are: hep-lat
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The categories of this paper are: gr-qc
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The categories of this paper are: hep-ph
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The categories of this paper are: bayes-an
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The categories of this paper are: ao-sci
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The categories of this paper are: solv-int
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The categories of this paper are: mtrl-th
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The categories of this paper are: patt-sol
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The categories of this paper are: hep-th
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The categories of this paper are: econ
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The categories of this paper are: chem-ph