  We introduce inverse reinforcement learning (IRL) as an effective paradigm
for training abstractive summarization models, imitating human summarization
behaviors. Our IRL model estimates the reward function using a suite of
important sub-rewards for summarization and concurrently optimizes the policy
network. Experimental results across datasets in different domains
(CNN/DailyMail and WikiHow) and various model sizes (BART-base and BART-large)
demonstrate the superiority of our proposed IRL model for summarization over
MLE and RL baselines. The resulting summaries exhibit greater similarity to
human-crafted gold references, outperforming MLE and RL baselines on metrics
such as ROUGE, coverage, novelty, compression ratio, factuality, and human
evaluations.
