Better Rewards Yield Better Summaries: Learning to Summarise Without References
Table 3: Full-length ROUGE F-scores of some recent RL-based (upper) and supervised (middle) extractive summarisation systems, as well as our system with learned rewards (bottom). R-1/2/L stands for ROUGE-1/2/L. Our system maximises the learned reward instead of ROUGE, hence receives lower ROUGE scores.
['System', 'Reward', 'R-1', 'R-2', 'R-L']
Table 3 presents the ROUGE scores of our system (NeuralTD+LearnedRewards) and multiple stateof-the-art systems. The summaries generated by our system receive decent ROUGE metrics, but are lower than most of the recent systems, because our learned reward is optimised towards high correlation with human judgement instead of ROUGE metrics.