# TREC 2020 Podcasts Track Overview

arXiv: https://arxiv.org/abs/2103.15953 · HTML: https://mlsanity.com/paper/2103.15953/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 34 references that are in the map.
- First posted on arXiv 29 Mar 2021.

## Builds on (most cited first, 4 of 4)

- [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)
- [Passage Re-ranking with BERT](https://mlsanity.com/paper/1901.04085/) (2019)
- [Detecting Extraneous Content in Podcasts](https://mlsanity.com/paper/2103.02585/) (2021)

## Built on by (most cited first, 13 of 13)

- [MQAG: Multiple-choice Question Answering and Generation for Assessing Information Consistency in Summarization](https://mlsanity.com/paper/2301.12307/) (2023)
- [Long-Span Summarization via Local Attention and Content Selection](https://mlsanity.com/paper/2105.03801/) (2021)
- [Introducing Neural Bag of Whole-Words with ColBERTer: Contextualized Late Interactions using Enhanced Reduction](https://mlsanity.com/paper/2203.13088/) (2022)
- [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)
- [Improving Content Retrievability in Search with Controllable Query Generation](https://mlsanity.com/paper/2303.11648/) (2023)
- [Summarizing Speech: A Comprehensive Survey](https://mlsanity.com/paper/2504.08024/) (2025)
- [Podcast Summary Assessment: A Resource for Evaluating Summary Assessment Methods](https://mlsanity.com/paper/2208.13265/) (2022)
- [Sparsity and Sentence Structure in Encoder-Decoder Attention of Summarization Systems](https://mlsanity.com/paper/2109.03888/) (2021)
- [Towards Proactive Information Retrieval in Noisy Text with Wikipedia Concepts](https://mlsanity.com/paper/2210.09877/) (2022)
- [Unsupervised Speaker Diarization that is Agnostic to Language, Overlap-Aware, and Tuning Free](https://mlsanity.com/paper/2207.12504/) (2022)
- [Rhapsody: A Dataset for Highlight Detection in Podcasts](https://mlsanity.com/paper/2505.19429/) (2025)
- [Listening Between the Lines: Decoding Podcast Narratives with Language Modeling](https://mlsanity.com/paper/2511.05310/) (2025)

## Abstract (arXiv)

The Podcast Track is new at the Text Retrieval Conference (TREC) in 2020. The podcast track was designed to encourage research into podcasts in the information retrieval and NLP research communities. The track consisted of two shared tasks: segment retrieval and summarization, both based on a dataset of over 100,000 podcast episodes (metadata, audio, and automatic transcripts) which was released concurrently with the track. The track generated considerable interest, attracted hundreds of new registrations to TREC and fifteen teams, mostly disjoint between search and summarization, made final submissions for assessment. Deep learning was the dominant experimental approach for both search experiments and summarization. This paper gives an overview of the tasks and the results of the participants' experiments. The track will return to TREC 2021 with the same two tasks, incorporating slight modifications in response to participant feedback.