  Automatic topic classification has been studied extensively to assist
managing and indexing scientific documents in a digital collection. With the
large number of topics being available in recent years, it has become necessary
to arrange them in a hierarchy. Therefore, the automatic classification systems
need to be able to classify the documents hierarchically. In addition, each
paper is often assigned to more than one relevant topic. For example, a paper
can be assigned to several topics in a hierarchy tree. In this paper, we
introduce a new dataset for hierarchical multi-label text classification
(HMLTC) of scientific papers called SciHTC, which contains 186,160 papers and
1,233 categories from the ACM CCS tree. We establish strong baselines for HMLTC
and propose a multi-task learning approach for topic classification with
keyword labeling as an auxiliary task. Our best model achieves a Macro-F1 score
of 34.57% which shows that this dataset provides significant research
opportunities on hierarchical scientific topic classification. We make our
dataset and code available on Github.
