# Detecting Extraneous Content in Podcasts

arXiv: https://arxiv.org/abs/2103.02585 · HTML: https://mlsanity.com/paper/2103.02585/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 27 references that are in the map.
- First posted on arXiv 3 Mar 2021.

## Builds on (most cited first, 8 of 8)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [HuggingFace's Transformers: State-of-the-art Natural Language Processing](https://mlsanity.com/paper/1910.03771/) (2019)
- [Teaching Machines to Read and Comprehend](https://mlsanity.com/paper/1506.03340/) (2015)
- [Pretrained Language Models for Sequential Sentence Classification](https://mlsanity.com/paper/1909.04054/) (2019)
- [SpeechBERT: An Audio-and-text Jointly Learned Language Model for End-to-end Spoken Question Answering](https://mlsanity.com/paper/1910.11559/) (2019)
- [Multimodal Content Analysis for Effective Advertisements on YouTube](https://mlsanity.com/paper/1709.03946/) (2017)
- [A Baseline Analysis for Podcast Abstractive Summarization](https://mlsanity.com/paper/2008.10648/) (2020)

## Built on by (most cited first, 7 of 7)

- [TREC 2020 Podcasts Track Overview](https://mlsanity.com/paper/2103.15953/) (2021)
- [Human-centered NLP Fact-checking: Co-Designing with Fact-checkers using Matchmaking for AI](https://mlsanity.com/paper/2308.07213/) (2023)
- [Towards Abstractive Grounded Summarization of Podcast Transcripts](https://mlsanity.com/paper/2203.11425/) (2022)
- [Cem Mil Podcasts: A Spoken Portuguese Document Corpus For Multi-modal, Multi-lingual and Multi-Dialect Information Access Research](https://mlsanity.com/paper/2209.11871/) (2022)
- [Modeling Language Usage and Listener Engagement in Podcasts](https://mlsanity.com/paper/2106.06605/) (2021)
- [Spotify at TREC 2020: Genre-Aware Abstractive Podcast Summarization](https://mlsanity.com/paper/2104.03343/) (2021)
- [The Case for a Legal Compliance API for the Enforcement of the EU's Digital Services Act on Social Media Platforms](https://mlsanity.com/paper/2205.06666/) (2022)

## 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.