  While large pretrained Transformer models have proven highly capable at
tackling natural language tasks, handling long sequence inputs continues to be
a significant challenge. One such task is long input summarization, where
inputs are longer than the maximum input context of most pretrained models.
Through an extensive set of experiments, we investigate what model
architectural changes and pretraining paradigms can most efficiently adapt a
pretrained Transformer for long input summarization. We find that a staggered,
block-local Transformer with global encoder tokens strikes a good balance of
performance and efficiency, and that an additional pretraining phase on long
sequences meaningfully improves downstream summarization performance. Based on
our findings, we introduce PEGASUS-X, an extension of the PEGASUS model with
additional long input pretraining to handle inputs of up to 16K tokens.
PEGASUS-X achieves strong performance on long input summarization tasks
comparable with much larger models while adding few additional parameters and
not requiring model parallelism to train.
