Human readable format for clusters of word vectors
The usual way is to present the top N (e.g. top 10) words for the cluster:

With distance-based clustering like K-means, the top words can be picked as the closest ones to the centroid.
With probabilistic methods such as LDA, the top words are the ones with the highest probability for the topic.