How evaluate text clustering?
Check out this paper.  It also addresses question of how many clusters to use.  The R package mclust has a routine which will try different cluster models/number of clusters and plot the Bayesian inference criterion (BIC). (great vignette here).  It's a general method, meaning, something you can do without being domain/data specific.  (It's always good to be domain-specific if you have the time and data.)

The chart is from the vignette by Lucca Scrucca.  MClust tries 14 different clustering algorithms (represented by the different symbols), increasing the number of clusters from 1 to some default value.  It's finds the BIC each time.  Highest BIC is usually the best choice.  You could apply this methodology to your own stable of clustering algorithms.