Research Article
Deep Learning Based Abstractive Text Summarization: Approaches, Datasets, Evaluation Measures, and Challenges
Table 5
Evaluation measures of several deep learning abstractive text summarisation methods over the Gigaword dataset.
| Reference | Year | Authors | Model | ROUGE1 | ROUGE2 | ROUGE-L |
| [18] | 2015 | Rush et al. | ABS+ | 28.18 | 8.49 | 23.81 | [39] | 2016 | Chopra et al. | RAS-Elman (kā=ā10) | 28.97 | 8.26 | 24.06 | [55] | 2016 | Nallapati et al. | Words-lvt5k-1sent | 28.61 | 9.42 | 25.24 | [52] | 2017 | Zhou et al. | SEASS | 36.15 | 17.54 | 33.63 | [53] | 2018 | Cao et al. | FTSumg | 37.27 | 17.65 | 34.24 | [54] | 2019 | Cai et al. | RCT | 37.27 | 18.19 | 34.62 |
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