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Due to the exponential growth of scientific publications on the Web, there isa pressing need to tag each paper with fine-grained topics so that researcherscan track their interested fields of study rather than drowning in the wholeliterature. Scientific literature tagging is beyond a pure multi-label textclassification task because papers on the Web are prevalently accompanied bymetadata information such as venues, authors, and references, which may serveas additional signals to infer relevant tags. Although there have been studiesmaking use of metadata in academic paper classification, their focus is oftenrestricted to one or two scientific fields (e.g., computer science andbiomedicine) and to one specific model. In this work, we systematically studythe effect of metadata on scientific literature tagging across 19 fields. Weselect three representative multi-label classifiers (i.e., a bag-of-wordsmodel, a sequence-based model, and a pre-trained language model) and exploretheir performance change in scientific literature tagging when metadata are fedto the classifiers as additional features. We observe some ubiquitous patternsof metadata's effects across all fields (e.g., venues are consistentlybeneficial to paper tagging in almost all cases), as well as some uniquepatterns in fields other than computer science and biomedicine, which are notexplored in previous studies.

The Effect of Metadata on Scientific Literature Tagging: A Cross-Field Cross-Model Study