@inproceedings{reddy-etal-2021-detecting,
title = "Detecting Extraneous Content in Podcasts",
author = "Reddy, Sravana and
Yu, Yongze and
Pappu, Aasish and
Sivaraman, Aswin and
Rezapour, Rezvaneh and
Jones, Rosie",
editor = "Merlo, Paola and
Tiedemann, Jorg and
Tsarfaty, Reut",
booktitle = "Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume",
month = apr,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.eacl-main.99/",
doi = "10.18653/v1/2021.eacl-main.99",
pages = "1166--1173",
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."
}
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<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.</abstract>
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%0 Conference Proceedings
%T Detecting Extraneous Content in Podcasts
%A Reddy, Sravana
%A Yu, Yongze
%A Pappu, Aasish
%A Sivaraman, Aswin
%A Rezapour, Rezvaneh
%A Jones, Rosie
%Y Merlo, Paola
%Y Tiedemann, Jorg
%Y Tsarfaty, Reut
%S Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume
%D 2021
%8 April
%I Association for Computational Linguistics
%C Online
%F reddy-etal-2021-detecting
%X 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.
%R 10.18653/v1/2021.eacl-main.99
%U https://aclanthology.org/2021.eacl-main.99/
%U https://doi.org/10.18653/v1/2021.eacl-main.99
%P 1166-1173
Markdown (Informal)
[Detecting Extraneous Content in Podcasts](https://aclanthology.org/2021.eacl-main.99/) (Reddy et al., EACL 2021)
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.