  Despite significant progress, state-of-the-art abstractive summarization
methods are still prone to hallucinate content inconsistent with the source
document. In this paper, we propose Constrained Abstractive Summarization
(CAS), a general setup that preserves the factual consistency of abstractive
summarization by specifying tokens as constraints that must be present in the
summary. We adopt lexically constrained decoding, a technique generally
applicable to autoregressive generative models, to fulfill CAS and conduct
experiments in two scenarios: (1) automatic summarization without human
involvement, where keyphrases are extracted from the source document and used
as constraints; (2) human-guided interactive summarization, where human
feedback in the form of manual constraints are used to guide summary
generation. Automatic and human evaluations on two benchmark datasets
demonstrate that CAS improves both lexical overlap (ROUGE) and factual
consistency of abstractive summarization. In particular, we observe up to 13.8
ROUGE-2 gains when only one manual constraint is used in interactive
summarization.
