Generating Wikipedia by Summarizing Long Sequences
Table 5: Linguistic quality human evaluation scores (scale 1-5, higher is better). A score significantly different (according to the Welch Two Sample t-test, with p=0.001) than the T-DMCA model is denoted by *.
['[BOLD] Model', '[BOLD] Focus', '[BOLD] Grammar', '[BOLD]  Non- redundancy ', '[BOLD]  Referential clarity ', '[BOLD]  Structure and Coherence ']
Human Evaluation - Linguistic quality Five different dimensions are assessed: grammaticality, non-redundancy, referential clarity, focus, and structure/coherence. Overall, we observed high fluency and coherence from our best abstractive model. Occasionally we observed some repetition of phrases which hurt the non-redundancy and structure, but it was much rarer compared with the other abstractive method, seq2seq. The biggest weakness of the extractive method compared with our best abstractive model was the lack of structure and coherence in the summaries.