  Production of news content is growing at an astonishing rate. To help manage
and monitor the sheer amount of text, there is an increasing need to develop
efficient methods that can provide insights into emerging content areas, and
stratify unstructured corpora of text into `topics' that stem intrinsically
from content similarity. Here we present an unsupervised framework that brings
together powerful vector embeddings from natural language processing with tools
from multiscale graph partitioning that can reveal natural partitions at
different resolutions without making a priori assumptions about the number of
clusters in the corpus. We show the advantages of graph-based clustering
through end-to-end comparisons with other popular clustering and topic
modelling methods, and also evaluate different text vector embeddings, from
classic Bag-of-Words to Doc2Vec to the recent transformers based model Bert.
This comparative work is showcased through an analysis of a corpus of US news
coverage during the presidential election year of 2016.
