A Unified Model for Extractive and Abstractive Summarization using Inconsistency Loss
Table 3: Comparing human evaluation results with state-of-the-art methods.
['Method', 'informativity', 'conciseness', 'readability']
The reference summaries get the best score on conciseness since the recent abstractive models tend to copy sentences from the input articles. However, our model learns well to select important information and form complete sentences so we even get slightly better scores on informativity and readability than the reference summaries. We show a typical example of our model comparing with other state-of-the-art methods in Fig. More examples (5 using CNN/Daily Mail news articles and 3 using non-news articles as inputs) are provided in the supplementary material.