# Diversity, Density, and Homogeneity: Quantitative Characteristic Metrics for Text Collections

arXiv: https://arxiv.org/abs/2003.08529 · HTML: https://mlsanity.com/paper/2003.08529/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Mar 2020.

## Built on by (most cited first, 22 of 22)

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- [We Need to Measure Data Diversity in NLP -- Better and Broader](https://mlsanity.com/paper/2505.20264/) (2025)
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- [Few-shot multi-token DreamBooth with LoRa for style-consistent character generation](https://mlsanity.com/paper/2510.09475/) (2025)
- [Measuring Dataset Diversity from a Geometric Perspective](https://mlsanity.com/paper/2602.09340/) (2026)
- [Want Better Synthetic Data? Steer It: Activation Steering for Low-Resource Language Generation](https://mlsanity.com/paper/2606.18389/) (2026)
- [LongCrafter: Towards Diverse Long-Context Understanding via Evidence-Graph-Guided Instruction Synthesis](https://mlsanity.com/paper/2607.06160/) (2026)
- [PLDiv: Measuring Dataset Diversity and Semantic Collapse via Persistence Landscapes](https://mlsanity.com/paper/2610.14031/) (2026)

## Abstract (arXiv)

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.