  MediaSum, a large-scale media interview dataset consisting of 463.6K
transcripts with abstractive summaries. To create this dataset, we collect
interview transcripts from NPR and CNN and employ the overview and topic
descriptions as summaries. Compared with existing public corpora for dialogue
summarization, our dataset is an order of magnitude larger and contains complex
multi-party conversations from multiple domains. We conduct statistical
analysis to demonstrate the unique positional bias exhibited in the transcripts
of televised and radioed interviews. We also show that MediaSum can be used in
transfer learning to improve a model's performance on other dialogue
summarization tasks.
