  The amount of information stored in the form of documents on the internet has
been increasing rapidly. Thus it has become a necessity to organize and
maintain these documents in an optimum manner. Text classification algorithms
study the complex relationships between words in a text and try to interpret
the semantics of the document. These algorithms have evolved significantly in
the past few years. There has been a lot of progress from simple machine
learning algorithms to transformer-based architectures. However, existing
literature has analyzed different approaches on different data sets thus making
it difficult to compare the performance of machine learning algorithms. In this
work, we revisit long document classification using standard machine learning
approaches. We benchmark approaches ranging from simple Naive Bayes to complex
BERT on six standard text classification datasets. We present an exhaustive
comparison of different algorithms on a range of long document datasets. We
re-iterate that long document classification is a simpler task and even basic
algorithms perform competitively with BERT-based approaches on most of the
datasets. The BERT-based models perform consistently well on all the datasets
and can be blindly used for the document classification task when the
computations cost is not a concern. In the shallow model's category, we suggest
the usage of raw BiLSTM + Max architecture which performs decently across all
the datasets. Even simpler Glove + Attention bag of words model can be utilized
for simpler use cases. The importance of using sophisticated models is clearly
visible in the IMDB sentiment dataset which is a comparatively harder task.

Computation and Language;Machine Learning