Weakly Supervised Multi-Label Classification of Full-Text Scientific
  Papers
Yu Zhang Bowen Jin Xiusi Chen Yanzhen Shen Yunyi Zhang Yu Meng Jiawei Han
Instead of relying on human-annotated training samples to build a classifier,
weakly supervised scientific paper classification aims to classify papers only
using category descriptions (e.g., category names, category-indicative
keywords). Existing studies on weakly supervised paper classification are less
concerned with two challenges: (1) Papers should be classified into not only
coarse-grained research topics but also fine-grained themes, and potentially
into multiple themes, given a large and fine-grained label space; and (2) full
text should be utilized to complement the paper title and abstract for
classification. Moreover, instead of viewing the entire paper as a long linear
sequence, one should exploit the structural information such as citation links
across papers and the hierarchy of sections and paragraphs in each paper. To
tackle these challenges, in this study, we propose FUTEX, a framework that uses
the cross-paper network structure and the in-paper hierarchy structure to
classify full-text scientific papers under weak supervision. A network-aware
contrastive fine-tuning module and a hierarchy-aware aggregation module are
designed to leverage the two types of structural signals, respectively.
Experiments on two benchmark datasets demonstrate that FUTEX significantly
outperforms competitive baselines and is on par with fully supervised
classifiers that use 1,000 to 60,000 ground-truth training samples.