Neural abstractive summarization has been widely studied and achieved great
success with large-scale corpora. However, the considerable cost of annotating
data motivates the need for learning strategies under low-resource settings. In
this paper, we investigate the problems of learning summarizers with only few
examples and propose corresponding methods for improvements. First, typical
transfer learning methods are prone to be affected by data properties and
learning objectives in the pretext tasks. Therefore, based on pretrained
language models, we further present a meta learning framework to transfer
few-shot learning processes from source corpora to the target corpus. Second,
previous methods learn from training examples without decomposing the content
and preference. The generated summaries could therefore be constrained by the
preference bias in the training set, especially under low-resource settings. As
such, we propose decomposing the contents and preferences during learning
through the parameter modulation, which enables control over preferences during
inference. Third, given a target application, specifying required preferences
could be non-trivial because the preferences may be difficult to derive through
observations. Therefore, we propose a novel decoding method to automatically
estimate suitable preferences and generate corresponding summary candidates
from the few training examples. Extensive experiments demonstrate that our
methods achieve state-of-the-art performance on six diverse corpora with
30.11%/33.95%/27.51% and 26.74%/31.14%/24.48% average improvements on
ROUGE-1/2/L under 10- and 100-example settings.

通过预训练语言模型建立的元学习框架，采用参数调节分解内容和偏好，并提出一种解码方法，可在只有少量的训练数据的情况下获得极好的效果，大幅提高了摘要生成质量。