Improving Abstractive Text Summarization with History Aggregation
Table 5: Human evaluation of three models. We compare the average score of salient, fluency and non-repeated. The best scores are bolded.
['Model', 'Salient', 'Fluency', 'Non-Repeated']
Human Evaluation We only compared three models on salient, fluency and non-repeated criteria, and our model gets the highest score in all criteria. But in fluency criterion, none of the models scores well, which means it is hard to understand semantic information for all models now. The Pointer Generator is our baseline abstractive summarization approach and has the lowest scores. The Pointer Generator uses the coverage mechanism to avoid generating overlap words, which can make summaries more fluent and less repetitive. The transformer is a new abstractive summarization based on attention mechanism, and it can get better performance than the Pointer Generator model. We equip the Transformer model with the aggregation mechanism, and it can get great improvement on all 3 criteria.