Boosting Factual Correctness of Abstractive Summarization with Knowledge Graph
Table 7: Human evaluation results of summaries for 100 randomly sampled articles in CNN/DailyMail test set. *p-value<0.05.
['Model', 'Factual Score', 'Informativeness']
We conduct a statistical test and find that compared with UniLM, our model ’s score is statistically significant with p-value smaller than 0.05 under paired t-test. In terms of informativeness, our model is comparable with UniLM. Finally, without the knowledge graph component, the Seq2Seq model generates summaries with both less factual correctness and informativeness.