  This paper contains the description of our submissions to the summarization
task of the Podcast Track in TREC (the Text REtrieval Conference) 2020. The
goal of this challenge was to generate short, informative summaries that
contain the key information present in a podcast episode using automatically
generated transcripts of the podcast audio. Since podcasts vary with respect to
their genre, topic, and granularity of information, we propose two
summarization models that explicitly take genre and named entities into
consideration in order to generate summaries appropriate to the style of the
podcasts. Our models are abstractive, and supervised using creator-provided
descriptions as ground truth summaries. The results of the submitted summaries
show that our best model achieves an aggregate quality score of 1.58 in
comparison to the creator descriptions and a baseline abstractive system which
both score 1.49 (an improvement of 9%) as assessed by human evaluators.
