  Summarizing data samples by quantitative measures has a long history, with
descriptive statistics being a case in point. However, as natural language
processing methods flourish, there are still insufficient characteristic
metrics to describe a collection of texts in terms of the words, sentences, or
paragraphs they comprise. In this work, we propose metrics of diversity,
density, and homogeneity that quantitatively measure the dispersion, sparsity,
and uniformity of a text collection. We conduct a series of simulations to
verify that each metric holds desired properties and resonates with human
intuitions. Experiments on real-world datasets demonstrate that the proposed
characteristic metrics are highly correlated with text classification
performance of a renowned model, BERT, which could inspire future applications.
