تقرير
Detecting Extraneous Content in Podcasts
| العنوان: | Detecting Extraneous Content in Podcasts |
|---|---|
| المؤلفون: | Reddy, Sravana, Yu, Yongze, Pappu, Aasish, Sivaraman, Aswin, Rezapour, Rezvaneh, Jones, Rosie |
| سنة النشر: | 2021 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Computer Science - Computation and Language |
| الوصف: | 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. Comment: EACL 2021 |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2103.02585 |
| رقم الأكسشن: | edsarx.2103.02585 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |