Learning to Extract Coherent Summary via Deep Reinforcement Learning
Table 2: Performance comparison on CNN/Daily Mail test set, evaluated with full-length F1 ROUGE scores (%). All scores of RNES are statistically significant using 95% confidence interval with respect to previous best models.
['Model', 'R-1', 'R-2', 'R-L']
At test time our model produces summaries by beam search with beam size 10. We use full-length F1 of ROUGE-1, ROUGE-2, and ROUGE-L to evaluate our model. Our RNES models (with or without coherence as the reward) outperform current state-of-the-art models and NES by a large margin. The result indicates that the summaries extracted by RNES are of higher quality than summaries produced by previous works.