mlsanityData to 7 Oct 2026

Detecting Extraneous Content in Podcasts

arXiv 2103.02585 ↗First posted 3 Mar 2021cs.CLcited by 7

Not on the map. Why?

Abstract (arXiv)

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.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 8 of its 27 references that are in the map. First posted on arXiv 3 Mar 2021.

Builds on

8 of 27 references.

References · 8

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2019BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehensioncited by 5,874
  3. 2019HuggingFace's Transformers: State-of-the-art Natural Language Processingcited by 2,201
  4. 2015Teaching Machines to Read and Comprehendcited by 1,039
  5. 2019Pretrained Language Models for Sequential Sentence Classificationcited by 53
  6. 2019SpeechBERT: An Audio-and-text Jointly Learned Language Model for End-to-end Spoken Question Answeringcited by 11
  7. 2017Multimodal Content Analysis for Effective Advertisements on YouTubecited by 5
  8. 2020A Baseline Analysis for Podcast Abstractive Summarizationcited by 1

Built on by

Cited by year

Citing papers per year: 2021: 3; 2022: 3; 2023: 1.

7 papers.

  1. 2021TREC 2020 Podcasts Track Overviewcited by 13
  2. 2023Human-centered NLP Fact-checking: Co-Designing with Fact-checkers using Matchmaking for AIcited by 9
  3. 2022Towards Abstractive Grounded Summarization of Podcast Transcriptscited by 8
  4. 2022Cem Mil Podcasts: A Spoken Portuguese Document Corpus For Multi-modal, Multi-lingual and Multi-Dialect Information Access Researchcited by 6
  5. 2021Modeling Language Usage and Listener Engagement in Podcastscited by 2
  6. 2021Spotify at TREC 2020: Genre-Aware Abstractive Podcast Summarizationcited by 0
  7. 2022The Case for a Legal Compliance API for the Enforcement of the EU's Digital Services Act on Social Media Platformscited by 0