Reward Learning for Efficient Reinforcement Learning in Extractive Document Summarisation
Table 2: The correlation of approximated and ground-truth ranking. ^σUx has significantly higher correlation over all other approaches.
['[EMPTY]', 'DUC’01 <italic>ρ</italic>', 'DUC’01 ndcg', 'DUC’02 <italic>ρ</italic>', 'DUC’02 ndcg', 'DUC’04 <italic>ρ</italic>', 'DUC’04 ndcg']
Table 2 compares the quality of our ^(cid:27)U x with other widely used rewards for input-specific RL (see x4). ^(cid:27)U x has significantly higher correlation to the ground-truth ranking compared with all other approaches, confirming that our proposed L2R method yields a superior reward oracle.