  Long document summarization is an important and hard task in the field of
natural language processing. A good performance of the long document
summarization reveals the model has a decent understanding of the human
language. Currently, most researches focus on how to modify the attention
mechanism of the transformer to achieve a higher ROUGE score. The study of data
pre-processing and post-processing are relatively few. In this paper, we use
two pre-processing methods and a post-processing method and analyze the effect
of these methods on various long document summarization models.
