Generating Wikipedia by Summarizing Long Sequences
Table 7: Comparison of results with Sauper & Barzilay (2009). Note our results are reported for lead section, whereas Sauper & Barzilay report for articles.
['[EMPTY]', '[BOLD] ROUGE-1 R', '[BOLD] ROUGE-1 P', '[BOLD] ROUGE-1 F1']
(in particular they make no claim of Wiki-clone detection). However, we make a best-effort comparison by finding the subset of articles of our test We observe that we perform better on American Actors than Diseases, probably because of the prevalence of the former (and biographies) in Wikipedia compared to the latter in our training set for our single, global model, whereas Sauper & Barzilay likely benefit from the category-specific templates. On average our ROUGE-1 scores are higher but do worse on the less common and somewhat specific disease category.