  The consumption of podcast media has been increasing rapidly. Due to the
lengthy nature of podcast episodes, users often carefully select which ones to
listen to. Although episode descriptions aid users by providing a summary of
the entire podcast, they do not provide a topic-by-topic breakdown. This study
explores the combined application of topic segmentation and text summarisation
methods to investigate how podcast episode comprehension can be improved. We
have sampled 10 episodes from Spotify's English-Language Podcast Dataset and
employed TextTiling and TextSplit to segment them. Moreover, three text
summarisation models, namely T5, BART, and Pegasus, were applied to provide a
very short title for each segment. The segmentation part was evaluated using
our annotated sample with the $P_k$ and WindowDiff ($WD$) metrics. A survey was
also rolled out ($N=25$) to assess the quality of the generated summaries. The
TextSplit algorithm achieved the lowest mean for both evaluation metrics
($\bar{P_k}=0.41$ and $\bar{WD}=0.41$), while the T5 model produced the best
summaries, achieving a relevancy score only $8\%$ less to the one achieved by
the human-written titles.
