Detecting Extraneous Content in Podcasts
Spotify（声田）；Indiana University（印第安纳大学）
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
