Improving Abstractive Text Summarization with History Aggregation
Table 6: Experiments on our Chinese dataset. We only experiment on three baseline models and evaluate results with ROUGE F metrics. The best scores are bolded.
['Model', 'ROUGE-1', 'ROUGE-2', 'ROUGE-L']
Because the dataset does not contain many novel words where it is suitable for the Pointer Generator model. Our dataset contains (6.17, 14.51, 17.99, 20.10)% novel (1,2,3,4)-gram and 59.90% novel sentences; by comparison, the novel n-gram and sentences frequency of CNN/DM in And the Pointer Generator model generates summaries containing less novel words and sentences, which leads to high scores in our Chinese dataset. Finally, we compare our model with the Transformer baseline model, and our results improve 5.31 in ROUGE-1, 4.56 in ROUGE-2 and 5.19 in ROUGE-L scores.