DiscoFuse: A Large-Scale Dataset for Discourse-Based Sentence Fusion
Table 7: In-domain evaluation with breakdown by discourse phenomena. Performance of DfSport and DfWiki on the sports and Wikipedia development sets.
['[BOLD] Discourse phenomena', '[BOLD] DfSport  [ITALIC] Exact', '[BOLD] DfSport  [ITALIC] SARI', '[BOLD] DfWiki  [ITALIC] Exact', '[BOLD] DfWiki  [ITALIC] SARI']
Finally, we take advantage of the provided annotation of the different discourse phenomena within each example in DiscoFuse. Results show that structural discourse types, such as apposition and relative clause, are easier to learn with both high exact match and SARI scores. While differences with respect to SARI scores are not large between phenomena, exact match varies more. Anaphora and verb phrase coordination are more challenging, but still require matching of the same noun (the named entity or the subject). On the other hand, discourse types that involve connective prediction, such as sentence coordination and discourse connective, require semantic understanding, and performance is significantly lower. In addition, when two discourse types are required for fusion, performance drops dramatically.