Detecting Extraneous Content in Podcasts

Sravana Reddy, Yongze Yu, Aasish Pappu, Aswin Sivaraman, Rezvaneh Rezapour, Rosie Jones


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.
Anthology ID:
2021.eacl-main.99
Volume:
Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume
Month:
April
Year:
2021
Address:
Online
Venue:
EACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1166–1173
Language:
URL:
https://aclanthology.org/2021.eacl-main.99
DOI:
10.18653/v1/2021.eacl-main.99
Bibkey:
Cite (ACL):
Sravana Reddy, Yongze Yu, Aasish Pappu, Aswin Sivaraman, Rezvaneh Rezapour, and Rosie Jones. 2021. Detecting Extraneous Content in Podcasts. In Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pages 1166–1173, Online. Association for Computational Linguistics.
Cite (Informal):
Detecting Extraneous Content in Podcasts (Reddy et al., EACL 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.eacl-main.99.pdf