Detecting Extraneous Content in Podcasts
This addresses the issue of filtering irrelevant material for podcast listeners and summarization tools, but it is incremental as it applies existing methods to a new domain.
The paper tackled the problem of detecting extraneous content like advertisements in podcasts by developing classifiers that use textual and listening patterns, resulting in improved ROUGE scores and reduced extraneous content in summaries.
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.