  Podcasts have recently shown a rapid rise in popularity. Summarization of
podcast transcripts is of practical benefit to both content providers and
consumers. It helps consumers to quickly decide whether they will listen to the
podcasts and reduces the cognitive load of content providers to write
summaries. Nevertheless, podcast summarization faces significant challenges
including factual inconsistencies with respect to the inputs. The problem is
exacerbated by speech disfluencies and recognition errors in transcripts of
spoken language. In this paper, we explore a novel abstractive summarization
method to alleviate these challenges. Specifically, our approach learns to
produce an abstractive summary while grounding summary segments in specific
portions of the transcript to allow for full inspection of summary details. We
conduct a series of analyses of the proposed approach on a large podcast
dataset and show that the approach can achieve promising results. Grounded
summaries bring clear benefits in locating the summary and transcript segments
that contain inconsistent information, and hence significantly improve
summarization quality in both automatic and human evaluation metrics.
