Detecting Extraneous Content in Podcasts

• URL: http://arxiv.org/abs/2103.02585v1
• Date: Wed, 3 Mar 2021 18:30:50 GMT
• Title: Detecting Extraneous Content in Podcasts
• Authors: Sravana Reddy, Yongze Yu, Aasish Pappu, Aswin Sivaraman, Rezvaneh Rezapour, Rosie Jones
• Abstract summary: We present a model that leverage both textual and listening patterns to detect extraneous content in podcast descriptions and audio transcripts. We show that our models can substantively improve ROUGE scores and reduce the extraneous content generated in the summaries.
• Score: 6.335863593761816
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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