  The diverse nature, scale, and specificity of podcasts present a unique
challenge to content discovery systems. Listeners often rely on text
descriptions of episodes provided by the podcast creators to discover new
content. Some factors like the presentation style of the narrator and
production quality are significant indicators of subjective user preference but
are difficult to quantify and not reflected in the text descriptions provided
by the podcast creators. We propose the automated creation of podcast audio
summaries to aid in content discovery and help listeners to quickly preview
podcast content before investing time in listening to an entire episode. In
this paper, we present a method to automatically construct a podcast summary
via guidance from the text-domain. Our method performs two key steps, namely,
audio to text transcription and text summary generation. Motivated by a lack of
datasets for this task, we curate an internal dataset, find an effective scheme
for data augmentation, and design a protocol to gather summaries from
annotators. We fine-tune a PreSumm[10] model with our augmented dataset and
perform an ablation study. Our method achieves ROUGE-F(1/2/L) scores of
0.63/0.53/0.63 on our dataset. We hope these results may inspire future
research in this direction.
