  Podcast summarization is different from summarization of other data formats,
such as news, patents, and scientific papers in that podcasts are often longer,
conversational, colloquial, and full of sponsorship and advertising
information, which imposes great challenges for existing models. In this paper,
we focus on abstractive podcast summarization and propose a two-phase approach:
sentence selection and seq2seq learning. Specifically, we first select
important sentences from the noisy long podcast transcripts. The selection is
based on sentence similarity to the reference to reduce the redundancy and the
associated latent topics to preserve semantics. Then the selected sentences are
fed into a pre-trained encoder-decoder framework for the summary generation.
Our approach achieves promising results regarding both ROUGE-based measures and
human evaluations.
