Enhanced Seq2Seq Autoencoder via Contrastive Learning for Abstractive Text Summarization
This work addresses text summarization for NLP applications, but it is incremental as it builds on existing Transformer-based methods with added components.
The paper tackles abstractive text summarization by enhancing a seq2seq autoencoder with contrastive learning and document augmentation, resulting in improved ROUGE scores and human evaluation, achieving comparable performance to state-of-the-art systems on two datasets.
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.