  Language model (LM) pre-training has resulted in impressive performance and
sample efficiency on a variety of language understanding tasks. However, it
remains unclear how to best use pre-trained LMs for generation tasks such as
abstractive summarization, particularly to enhance sample efficiency. In these
sequence-to-sequence settings, prior work has experimented with loading
pre-trained weights into the encoder and/or decoder networks, but used
non-pre-trained encoder-decoder attention weights. We instead use a pre-trained
decoder-only network, where the same Transformer LM both encodes the source and
generates the summary. This ensures that all parameters in the network,
including those governing attention over source states, have been pre-trained
before the fine-tuning step. Experiments on the CNN/Daily Mail dataset show
that our pre-trained Transformer LM substantially improves over pre-trained
Transformer encoder-decoder networks in limited-data settings. For instance, it
achieves 13.1 ROUGE-2 using only 1% of the training data (~3000 examples),
while pre-trained encoder-decoder models score 2.3 ROUGE-2.
