  This paper introduces a model for incomplete utterance restoration (IUR)
called JET (\textbf{J}oint learning token \textbf{E}xtraction and \textbf{T}ext
generation). Different from prior studies that only work on extraction or
abstraction datasets, we design a simple but effective model, working for both
scenarios of IUR. Our design simulates the nature of IUR, where omitted tokens
from the context contribute to restoration. From this, we construct a Picker
that identifies the omitted tokens. To support the picker, we design two label
creation methods (soft and hard labels), which can work in cases of no
annotation data for the omitted tokens. The restoration is done by using a
Generator with the help of the Picker on joint learning. Promising results on
four benchmark datasets in extraction and abstraction scenarios show that our
model is better than the pretrained T5 and non-generative language model
methods in both rich and limited training data settings.\footnote{The code is
available at \url{https://github.com/shumpei19/JET}}
