A Unified Model for Extractive and Abstractive Summarization using Inconsistency Loss
Table 2: ROUGE F-1 scores of the generated abstractive summaries on the CNN/Daily Mail test set. Our two-stages model outperforms pointer-generator model on ROUGE-1 and ROUGE-2. In addition, our model trained end-to-end with inconsistency loss exceeds the lead-3 baseline. All our ROUGE scores have a 95% confidence interval with at most ±0.24. ‘∗’ indicates the model is trained and evaluated on the anonymized dataset and thus is not strictly comparable with ours.
['Method', 'ROUGE-1', 'ROUGE-2', 'ROUGE-L']
We use full-length ROUGE-1, ROUGE-2 and ROUGE-L F-1 scores to evaluate the generated summaries. We compare our models (two-stage and end-to-end) with state-of-the-art abstractive summarization models Nallapati et al. ; Paulus et al. Due to the writing style of news articles, the most important information is often written at the beginning of an article which makes lead-3 a strong baseline. After end-to-end training without inconsistency loss, our method already achieves better ROUGE scores by cooperating with each other. Moreover, our model end-to-end trained with inconsistency loss achieves state-of-the-art ROUGE scores and exceeds lead-3 baseline.