English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Summarizing data samples by quantitative measures has a long history, withdescriptive statistics being a case in point. However, as natural languageprocessing methods flourish, there are still insufficient characteristicmetrics to describe a collection of texts in terms of the words, sentences, orparagraphs 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 toverify that each metric holds desired properties and resonates with humanintuitions. Experiments on real-world datasets demonstrate that the proposedcharacteristic metrics are highly correlated with text classificationperformance of a renowned model, BERT, which could inspire future applications.

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