  Podcast summary, an important factor affecting end-users' listening
decisions, has often been considered a critical feature in podcast
recommendation systems, as well as many downstream applications. Existing
abstractive summarization approaches are mainly built on fine-tuned models on
professionally edited texts such as CNN and DailyMail news. Different from
news, podcasts are often longer, more colloquial and conversational, and
noisier with contents on commercials and sponsorship, which makes automatic
podcast summarization extremely challenging. This paper presents a baseline
analysis of podcast summarization using the Spotify Podcast Dataset provided by
TREC 2020. It aims to help researchers understand current state-of-the-art
pre-trained models and hence build a foundation for creating better models.

Computation and Language