A Two-Phase Approach for Abstractive Podcast Summarization
This work addresses podcast summarization, a domain-specific problem due to the unique challenges of long, conversational content, but it is incremental as it builds on existing seq2seq methods.
The authors tackled abstractive podcast summarization by proposing a two-phase approach involving sentence selection and seq2seq learning, which achieved promising results in ROUGE-based measures and human evaluations.
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.