Length-controllable Abstractive Summarization by Guiding with Summary Prototype
Table 3: ROUGE scores (F1) of abstractive summarization models with gold settings on the CNN/DM dataset.
['[EMPTY]', 'R-1', 'R-2', 'R-L']
We evaluated our model in the following two settings in order to analyze the relationship between the quality of the abstractive summary and that of the prototype. In the gold-length setting, we only gave the gold length K to the prototype extractor. In the gold sentences + the gold-length setting, we gave the gold sentences Soracle and gold length These results indicate that selecting the correct number of words in the prototype improves the ROUGE scores. In this study, we simply selected the average length when extracting the prototype for all examples in the standard setting; however, there will be an improvement if we adaptively select the number of words in the prototype for each source text. Moreover, the ROUGE score largely improved in the gold sentence and gold-length settings. This indicates that the quality of the generated summary will significantly improve by increasing the accuracy of the extractive model.