Efficient framework for low-resource abstractive summarization by meta-transfer learning and pointer-generator networks

Recently, large language models have shown great success on various abstractive summarization datasets. These datasets consist of numerous data that are enough to train a large number of parameters. However, for a new domain, there is a lack of labeled data to train those parameters and the model is easily overfitted to a small amount of data. In addition, because annotating document-summary pairs is too expensive and transfer learning using high-resource datasets causes a domain shifting problem, a low-resource abstractive summarization task is becoming necessary. Herein, we propose an efficient framework for low-resource abstractive summarization using a pointer-generator network and a meta-learning technique to address the above problems. Meta-learning using existing high-resource datasets enables our model to rapidly adapt to a new domain using limited data to solve the domain shifting problem. In addition, we explore the copy mechanism using a pointer-generator network that can copy words from a source document when generating a summary. The experimental results on 11 different datasets show that the proposed model outperforms the previous state-of-the-art models in low-resource abstractive summarization on most of the datasets.