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.
A. Ghazimatin, E. Garmash, G. Penha, K. Sheets, M. Achenbach, O. Semerci, R. Galvez, M. Tannenberg, S. Mantravadi, D. Narayanan, O. Kalaydzhyan, D. Cole, B. Carterette, A. Clifton, P. N. Bennett, C. Hauff, M. Lalmas-Roelleke
Ekaterina Garmash, Edgar Tanaka, Ann Clifton, Joana Correia, Sharmistha Jat, Winstead Zhu, Rosie Jones, Jussi Karlgren
Winstead Zhu, Md Iftekhar Tanveer, Yang Janet Liu, Seye Ojumu, Rosie Jones
