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.

该研究介绍了用于 TREC 2020 播客摘要挑战的方法。该方法采用了两个步骤：过滤摘要中的冗余或信息较少的句子，然后应用基于 BART 的文本摘要系统。最好的结果在人类评估中达到了 1.777 的得分。