  In this paper, we present a denoising sequence-to-sequence (seq2seq)
autoencoder via contrastive learning for abstractive text summarization. Our
model adopts a standard Transformer-based architecture with a multi-layer
bi-directional encoder and an auto-regressive decoder. To enhance its denoising
ability, we incorporate self-supervised contrastive learning along with various
sentence-level document augmentation. These two components, seq2seq autoencoder
and contrastive learning, are jointly trained through fine-tuning, which
improves the performance of text summarization with regard to ROUGE scores and
human evaluation. We conduct experiments on two datasets and demonstrate that
our model outperforms many existing benchmarks and even achieves comparable
performance to the state-of-the-art abstractive systems trained with more
complex architecture and extensive computation resources.
