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 (k = 10)	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