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In this paper, we describe our approach for the Podcast Summarisationchallenge in TREC 2020. Given a podcast episode with its transcription, thegoal is to generate a summary that captures the most important information inthe content. Our approach consists of two steps: (1) Filtering redundant orless informative sentences in the transcription using the attention of ahierarchical 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 submissionruns. We also fine-tune the BART model on the Podcast data as our baseline. Thehuman evaluation by NIST shows that our best submission achieves 1.777 in theEGFB scale, while the score of creator-provided description is 1.291. Oursystem won the Spotify Podcast Summarisation Challenge in the TREC2020 PodcastTrack in both human and automatic evaluation.

CUED_speech at TREC 2020 Podcast Summarisation Track