Detecting Extraneous Content in Podcasts

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.

Related

September 2022 | Interspeech

Unsupervised Speaker Diarization that is Agnostic to Language Overlap Aware and Free of Tuning

M Iftekhar Tanveer, Diego Casabuena, Jussi Karlgren, Rosie Jones

September 2022 | Interspeech

Exploring audio-based stylistic variation in podcasts

Katariina Martikainen, Jussi Karlgren, Khiet Truong

July 2022 | SIGIR

What Makes a Good Podcast Summary?

Rezvaneh Rezapour, Sravana Reddy, Ann Clifton, Rosie Jones