Improving Abstractive Text Summarization with History Aggregation
Table 1: Comparison of different model results on CNN/DaliyMail test dataset using F1 scores of ROUGE-1, ROUGE-2, ROUGE-L with 95% confidence interval. The first part is previous abstractive baseline models, the second part is the Transformer baseline model and our Transformer model with aggregation mechanism. The best scores are bolded.
['Model', 'ROUGE-1', 'ROUGE-2', 'ROUGE-L']
Quantitative Analysis Overall, our model improves all other baselines(reported in their articles) for ROUGE-1, 2 F1 scores, while our model gets a lower ROUGE-L F1 score than the RL (Reinforcement Learning) model[chen2018fast]. The novel words are harmful to ROUGE-2, L F1 scores. This result also account for our models being more abstractive.