Semantic WordRank: Generating Finer Single-Document Summarizations
Table 3: ROUGE (%) comparison with different features removed
['[BOLD] Methods', '[BOLD] R-1 10%', '[BOLD] R-2 10%', '[BOLD] R-SU4 10%', '[BOLD] R-1 40%', '[BOLD] R-2 40%', '[BOLD] R-SU4 40%', '[BOLD] R-1 70%', '[BOLD] R-2 70%', '[BOLD] R-SU4 70%']
TextRank is added as a baseline model. We can see that when we remove a feature, the ROUGE scores drop, indicating that each part is essential. The numbers in bold are the biggest drops. We also see that when the summaries are short, removing article structure results in much larger drops, indicating that article structures are a more significant feature than the other two features. As summaries become longer, including semantic edges would become increasingly more critical. Our results indicate that using the Softplus function adjustment does improve the ROUGE scores, but not as significant as using the other three features.