We survey the literature on single-label, multi-label, and hierarchical text classification and provide a quantitative comparison of methods categorized into bag-of-words, sequence-based, and graph- or hierarchy-based approaches. Despite a recent surge in graph-based methods, they do not provide an improvement over fine-tuned transformer models on most evaluated datasets. Decoder-only generative language models show promise in few-shot in-context learning, but appear to lag behind fine-tuned language models when sufficient training data is available. The amount of training data needed for a fine-tuned language model to exceed the performance of a generative model is task-dependent. We further highlight the variance in reported numbers across the literature when applying the same model to the same dataset, which can be traced to the use of different hyperparameter values, such as the fine-tuning learning rate. For practitioners, we recommend using a fine-tuned language model when sufficient training data is available. Otherwise, a frozen generative model, enhanced by few-shot in-context learning or reasoning, is preferable. The source code and further information are available at: https://github.com/ascherp/text-classification-survey

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