Recent advances in the field of abstractive summarization leverage
pre-trained language models rather than train a model from scratch. However,
such models are sluggish to train and accompanied by a massive overhead.
Researchers have proposed a few lightweight alternatives such as smaller
adapters to mitigate the drawbacks. Nonetheless, it remains uncertain whether
using adapters benefits the task of summarization, in terms of improved
efficiency without an unpleasant sacrifice in performance. In this work, we
carry out multifaceted investigations on fine-tuning and adapters for
summarization tasks with varying complexity: language, domain, and task
transfer. In our experiments, fine-tuning a pre-trained language model
generally attains a better performance than using adapters; the performance gap
positively correlates with the amount of training data used. Notably, adapters
exceed fine-tuning under extremely low-resource conditions. We further provide
insights on multilinguality, model convergence, and robustness, hoping to shed
light on the pragmatic choice of fine-tuning or adapters in abstractive
summarization.

本文对使用 fine-tuning 和 adapters 在不同复杂性的摘要任务中进行了多方面的研究，并且研究发现 fine-tuning 通常比使用 adapters 获得更好的性能，而 adapters 在极低资源条件下超越 fine-tuning。