# CUED_speech at TREC 2020 Podcast Summarisation Track

arXiv: https://arxiv.org/abs/2012.02535 · HTML: https://mlsanity.com/paper/2012.02535/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Dec 2020.

## Built on by (most cited first, 5 of 5)

- [Long-Span Summarization via Local Attention and Content Selection](https://mlsanity.com/paper/2105.03801/) (2021)
- [Towards Abstractive Grounded Summarization of Podcast Transcripts](https://mlsanity.com/paper/2203.11425/) (2022)
- [CUED at ProbSum 2023: Hierarchical Ensemble of Summarization Models](https://mlsanity.com/paper/2306.05317/) (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)

## Abstract (arXiv)

In this paper, we describe our approach for the Podcast Summarisation challenge in TREC 2020. Given a podcast episode with its transcription, the goal is to generate a summary that captures the most important information in the content. Our approach consists of two steps: (1) Filtering redundant or less informative sentences in the transcription using the attention of a hierarchical model; (2) Applying a state-of-the-art text summarisation system (BART) fine-tuned on the Podcast data using a sequence-level reward function. Furthermore, we perform ensembles of three and nine models for our submission runs. We also fine-tune the BART model on the Podcast data as our baseline. The human evaluation by NIST shows that our best submission achieves 1.777 in the EGFB scale, while the score of creator-provided description is 1.291. Our system won the Spotify Podcast Summarisation Challenge in the TREC2020 Podcast Track in both human and automatic evaluation.