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Large-scale learning of transformer language models has yielded improvementson a variety of natural language understanding tasks. Whether they can beeffectively adapted for summarization, however, has been less explored, as thelearned representations are less seamlessly integrated into existing neuraltext production architectures. In this work, we propose two solutions forefficiently adapting pretrained transformer language models as textsummarizers: source embeddings and domain-adaptive training. We test thesesolutions on three abstractive summarization datasets, achieving new state ofthe art performance on two of them. Finally, we show that these improvementsare achieved by producing more focused summaries with fewer superfluous andthat performance improvements are more pronounced on more abstractive datasets.

Efficient Adaptation of Pretrained Transformers for Abstractive Summarization