  Contrastive learning models have achieved great success in unsupervised
visual representation learning, which maximize the similarities between feature
representations of different views of the same image, while minimize the
similarities between feature representations of views of different images. In
text summarization, the output summary is a shorter form of the input document
and they have similar meanings. In this paper, we propose a contrastive
learning model for supervised abstractive text summarization, where we view a
document, its gold summary and its model generated summaries as different views
of the same mean representation and maximize the similarities between them
during training. We improve over a strong sequence-to-sequence text generation
model (i.e., BART) on three different summarization datasets. Human evaluation
also shows that our model achieves better faithfulness ratings compared to its
counterpart without contrastive objectives.
