Due to the exponential growth of scientific publications on the Web, there is
a pressing need to tag each paper with fine-grained topics so that researchers
can track their interested fields of study rather than drowning in the whole
literature. Scientific literature tagging is beyond a pure multi-label text
classification task because papers on the Web are prevalently accompanied by
metadata information such as venues, authors, and references, which may serve
as additional signals to infer relevant tags. Although there have been studies
making use of metadata in academic paper classification, their focus is often
restricted to one or two scientific fields (e.g., computer science and
biomedicine) and to one specific model. In this work, we systematically study
the effect of metadata on scientific literature tagging across 19 fields. We
select three representative multi-label classifiers (i.e., a bag-of-words
model, a sequence-based model, and a pre-trained language model) and explore
their performance change in scientific literature tagging when metadata are fed
to the classifiers as additional features. We observe some ubiquitous patterns
of metadata's effects across all fields (e.g., venues are consistently
beneficial to paper tagging in almost all cases), as well as some unique
patterns in fields other than computer science and biomedicine, which are not
explored in previous studies.

本文系统研究了 19 个领域中的元数据对科学文献标记的影响，并通过三种代表性多标签分类器探索它们在元数据作为附加特征时的性能变化，发现在所有领域中都存在有关元数据的普遍模式，以及在计算机科学和生物医学以外的领域中存在一些独特的模式。