  Interleaved texts, where posts belonging to different threads occur in a
sequence, commonly occur in online chat posts, so that it can be time-consuming
to quickly obtain an overview of the discussions. Existing systems first
disentangle the posts by threads and then extract summaries from those threads.
A major issue with such systems is error propagation from the disentanglement
component. While end-to-end trainable summarization system could obviate
explicit disentanglement, such systems require a large amount of labeled data.
To address this, we propose to pretrain an end-to-end trainable hierarchical
encoder-decoder system using synthetic interleaved texts. We show that by
fine-tuning on a real-world meeting dataset (AMI), such a system out-performs a
traditional two-step system by 22%. We also compare against transformer models
and observed that pretraining with synthetic data both the encoder and decoder
outperforms the BertSumExtAbs transformer model which pretrains only the
encoder on a large dataset.
