Reward Learning for Efficient Reinforcement Learning in Extractive Document Summarisation
Table 3: Results of non-RL (top), cross-input (DeepTD) and input-specific (REAPER) RL approaches (middle) compared with RELIS.
['[EMPTY]', 'DUC’01 <italic>R</italic>1', 'DUC’01 <italic>R</italic>2', 'DUC’02 <italic>R</italic>1', 'DUC’02 <italic>R</italic>2', 'DUC’04 <italic>R</italic>1', 'DUC’04 <italic>R</italic>2']
In Table 3, we compare RELIS with non-RL-based and RL-based summarisation systems. For non-RL-based systems, we report ICSI [Gillick and Favre, 2009] maximising [CONTINUE] the bigram overlap of summary and input using integer linear programming, PriorSum [Cao et al., 2015] learning sentence quality with CNNs, TCSum [Cao et al., 2017] employing text classification of the input documents, the variant of TCSum without the text classification pre-training (TCSum(cid:0)) and SRSum [Ren et al., 2018], which learns sentence relations with both word- and sentence-level attentive neural networks to estimate salience. [CONTINUE] RELIS significantly outperforms the other RL-based systems. Note that RELIS and REAPER use the identical RL algorithm for input-specific policy learning; hence the improvement of RELIS is due to the higher quality of the L2R-learnt reward ^(cid:27)U x . RELIS outperforms DeepTD because training cross-input policies requires much more data than available in the DUC datasets. At the same time, RELIS performs on par with neural-based TCSum and SRSum, while it requires significantly less data and time to train, as shown next.