  Podcasts have emerged as a massively consumed online content, notably due to
wider accessibility of production means and scaled distribution through large
streaming platforms. Categorization systems and information access technologies
typically use topics as the primary way to organize or navigate podcast
collections. However, annotating podcasts with topics is still quite
problematic because the assigned editorial genres are broad, heterogeneous or
misleading, or because of data challenges (e.g. short metadata text, noisy
transcripts). Here, we assess the feasibility to discover relevant topics from
podcast metadata, titles and descriptions, using topic modeling techniques for
short text. We also propose a new strategy to leverage named entities (NEs),
often present in podcast metadata, in a Non-negative Matrix Factorization (NMF)
topic modeling framework. Our experiments on two existing datasets from Spotify
and iTunes and Deezer, a new dataset from an online service providing a catalog
of podcasts, show that our proposed document representation, NEiCE, leads to
improved topic coherence over the baselines. We release the code for
experimental reproducibility of the results.
