  Podcast episodes often contain material extraneous to the main content, such
as advertisements, interleaved within the audio and the written descriptions.
We present classifiers that leverage both textual and listening patterns in
order to detect such content in podcast descriptions and audio transcripts. We
demonstrate that our models are effective by evaluating them on the downstream
task of podcast summarization and show that we can substantively improve ROUGE
scores and reduce the extraneous content generated in the summaries.
