A Unified Model for Extractive and Abstractive Summarization using Inconsistency Loss
Table 1: ROUGE recall scores of the extracted sentences. pre-trained indicates the extractor trained on the ground-truth labels. end2end indicates the extractor after end-to-end training with the abstracter. Note that ground-truth labels show the upper-bound performance since the reference summary to calculate ROUGE-recall is abstractive. All our ROUGE scores have a 95% confidence interval with at most ±0.33.
['Method', 'ROUGE-1', 'ROUGE-2', 'ROUGE-L']
High ROUGE recall scores can be obtained if the extracted sentences include more words or sequences overlapping with the reference abstractive summary. For each article, we select sentences with the sentence probabilities β greater than 0.5. We show the results of the ground-truth sentence labels (Sec. Note that the ground-truth extracted sentences can’t get ROUGE recall scores of 100 because reference summary is abstractive and may contain some words and sequences that are not in the article. Our extractor performs the best when end-to-end trained with inconsistency loss.