Research Article
[Retracted] XAI-Based Reinforcement Learning Approach for Text Summarization of Social IoT-Based Content
Table 5
Evaluation results of generated summarization on CNN/daily mail dataset %.
| How the abstract is generated | Model method | ROUGE-1 | ROUGE-2 | ROUGE-L | ROUGE-AVG | METEOR |
| Extraction | MMS_Text | 47.57 | 25.72 | 44.42 | 39.24 | 26.97 | SummaRuNNer | 48.6 | 25.2 | 44.3 | 39.37 | 26.75 | Refresh | 49.27 | 27.2 | 45.6 | 40.69 | 27.38 | HSSAS | 51.3 | 26.8 | 46.6 | 41.57 | 28.27 |
| Generative | Pointer-generator + coverage | 48.53 | 26.28 | 45.38 | 40.06 | 27.7 | Bottom-up | 50.22 | 27.68 | 47.34 | 41.75 | 28.38 | DCA | 50.69 | 28.47 | 46.92 | 42.03 | 28.55 |
| Supervised | BERTSUMEXTABS | 51.13 | 28.6 | 48.18 | 42.64 | 28.91 | PEGASUSBASE | 50.79 | 27.81 | 47.93 | 42.18 | 28.63 | XAI-RL4 (Table 2 combination 4) | 51.64 | 29.13 | 49.33 | 43.37 | 29.7 | XAI-RL5 (Table 2 combination 5) | 52.01 | 29.28 | 49.71 | 43.67 | 29.48 | XAI-RL6 (Table 2 combination 6) | 53.29 | 30.55 | 40.13 | 44.66 | 20.51 |
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