  Many text generation tasks naturally contain two steps: content selection and
surface realization. Current neural encoder-decoder models conflate both steps
into a black-box architecture. As a result, the content to be described in the
text cannot be explicitly controlled. This paper tackles this problem by
decoupling content selection from the decoder. The decoupled content selection
is human interpretable, whose value can be manually manipulated to control the
content of generated text. The model can be trained end-to-end without human
annotations by maximizing a lower bound of the marginal likelihood. We further
propose an effective way to trade-off between performance and controllability
with a single adjustable hyperparameter. In both data-to-text and headline
generation tasks, our model achieves promising results, paving the way for
controllable content selection in text generation.
