  This study reviews and compares methods for single-label and multi-label text
classification, categorized into bag-of-words, sequence-based, graph-based, and
hierarchical methods. The comparison aggregates results from the literature
over five single-label and seven multi-label datasets and complements them with
new experiments. The findings reveal that all recently proposed graph-based and
hierarchy-based methods fail to outperform pre-trained language models and
sometimes perform worse than standard machine learning methods like a
multilayer perceptron on a bag-of-words. To assess the true scientific progress
in text classification, future work should thoroughly test against strong
bag-of-words baselines and state-of-the-art pre-trained language models.
