What Gets Echoed? Understanding the “Pointers” in Explanations of Persuasive Arguments
Table 6: ROUGE scores (F1) on the test dataset (Lin, 2004). The differences in ROUGE-1 and ROUGE-L are statistically significant with p<1e−10.
['[EMPTY]', 'ROUGE-1', 'ROUGE-2', 'ROUGE-L']
Specifically, their use leads to statistically significant improvement in ROUGE-1 and ROUGE-L, while slightly hurting the performance in ROUGE-2 (the difference is not statistically significant). We also find that our features can increase the likelihood of copying: an average of 17.59 unique words get copied to the generated explanation with our features, compared to 14.17 unique words without our features. For comparison, target explanations have an average of 34.81 unique words. We emphasize that generating explanations is a very challenging task (evidenced by the low ROUGE scores and examples in the supplementary material), and that fully solving the generation task requires more work.