Conference proceeding
Detecting Extraneous Content in Podcasts
16TH CONFERENCE OF THE EUROPEAN CHAPTER OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (EACL 2021), pp.1166-1173
01 Jan 2021
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.
Metrics
4 Record Views
Details
- Title
- Detecting Extraneous Content in Podcasts
- Creators
- Sravana Reddy - Spotify, Stockholm, SwedenYongze Yu - Spotify, Stockholm, SwedenAasish Pappu - Spotify, Stockholm, SwedenAswin Sivaraman - Spotify, Stockholm, SwedenRezvaneh Rezapour - Spotify, Stockholm, SwedenRosie Jones - Spotify, Stockholm, SwedenAssoc Computat Linguist
- Publication Details
- 16TH CONFERENCE OF THE EUROPEAN CHAPTER OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (EACL 2021), pp.1166-1173
- Conference
- 16TH CONFERENCE OF THE EUROPEAN CHAPTER OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (EACL 2021), 16th
- Publisher
- Assoc Computational Linguistics-Acl
- Number of pages
- 8
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Information Science (Informatics)
- Identifiers
- 991021861640904721
InCites Highlights
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- Collaboration types
- Industry collaboration
- Domestic collaboration
- International collaboration
- Web of Science research areas
- Computer Science, Artificial Intelligence
- Computer Science, Interdisciplinary Applications
- Linguistics