  Abstractive summarization aims at generating natural language summaries of a
source document that are succinct while preserving the important elements.
Despite recent advances, neural text summarization models are known to be
susceptible to hallucinating (or more correctly confabulating), that is to
produce summaries with details that are not grounded in the source document. In
this paper, we introduce a simple yet efficient technique, CoBa, to reduce
hallucination in abstractive summarization. The approach is based on two steps:
hallucination detection and mitigation. We show that the former can be achieved
through measuring simple statistics about conditional word probabilities and
distance to context words. Further, we demonstrate that straight-forward
backtracking is surprisingly effective at mitigation. We thoroughly evaluate
the proposed method with prior art on three benchmark datasets for text
summarization. The results show that CoBa is effective and efficient in
reducing hallucination, and offers great adaptability and flexibility.
