  Pre-trained transformer-based sequence-to-sequence models have become the
go-to solution for many text generation tasks, including summarization.
However, the results produced by these models tend to contain significant
issues such as hallucinations and irrelevant passages. One solution to mitigate
these problems is to incorporate better content planning in neural
summarization. We propose to use entity chains (i.e., chains of entities
mentioned in the summary) to better plan and ground the generation of
abstractive summaries. In particular, we augment the target by prepending it
with its entity chain. We experimented with both pre-training and finetuning
with this content planning objective. When evaluated on CNN/DailyMail, SAMSum
and XSum, models trained with this objective improved on entity correctness and
summary conciseness, and achieved state-of-the-art performance on ROUGE for
SAMSum and XSum.
