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.

本文提出了一种对比学习模型，用于文本摘要的有监督抽象，通过将文档、其黄金摘要和模型生成的摘要视为同一平均表示的不同视图，并在训练过程中最大化它们之间的相似性，从而改进了强序列到序列的文本生成模型（即 BART）。人类评估也显示，我们的模型与没有对比目标的对应模型相比，实现了更好的忠实度评分。