  Prior studies on text-to-text generation typically assume that the model
could figure out what to attend to in the input and what to include in the
output via seq2seq learning, with only the parallel training data and no
additional guidance. However, it remains unclear whether current models can
preserve important concepts in the source input, as seq2seq learning does not
have explicit focus on the concepts and commonly used evaluation metrics also
treat concepts equally important as other tokens. In this paper, we present a
systematic analysis that studies whether current seq2seq models, especially
pre-trained language models, are good enough for preserving important input
concepts and to what extent explicitly guiding generation with the concepts as
lexical constraints is beneficial. We answer the above questions by conducting
extensive analytical experiments on four representative text-to-text generation
tasks. Based on the observations, we then propose a simple yet effective
framework to automatically extract, denoise, and enforce important input
concepts as lexical constraints. This new method performs comparably or better
than its unconstrained counterpart on automatic metrics, demonstrates higher
coverage for concept preservation, and receives better ratings in the human
evaluation. Our code is available at https://github.com/morningmoni/EDE.
