mlsanityData to 14 Oct 2026

TREC 2020 Podcasts Track Overview

arXiv 2103.15953 ↗First posted 29 Mar 2021cs.IR cs.CLcited by 13

Not on the map. Why?

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.

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

4 of 34 references.

References · 4

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  2. 2019BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehensioncited by 5,893
  3. 2019Passage Re-ranking with BERTcited by 645
  4. 2021Detecting Extraneous Content in Podcastscited by 7

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Cited by year

Citing papers per year: 2021: 2; 2022: 6; 2023: 2; 2024: 0; 2025: 3.

13 papers.

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