Introducing Aspects of Creativity in Automatic Poetry Generation
Table 4: Average Coh-Metrix evaluations across 25 randomly selected poems from each model.
['[BOLD] Model', '[BOLD] FRE', '[BOLD] FKGL', '[BOLD] IMGc', '[BOLD] CNCc', '[BOLD] LDTTRa', '[BOLD] PCREFp', '[BOLD] PCSYNp', '[BOLD] PCNARp']
Inspection of our Coh-Metrix results show that randomly selected poems from all models fall at or below the 2nd-grade reading level (in FKGL scores) and are greater than 93 on the FRE scale. This suggests generated poems are easily readable by the majority of viewers. Looking at the IMGc and CNCc scores, we see that our poems, except for the dream model concreteness, fall in the 400s. Words with higher imageability and concreteness fall around the low 600s while words that are lower fall around the upper 200s on this scale. These scores reveal that our models are generating text that is concrete in word choice and that paint a picture. Our dream model scoring lower in the concreteness is reasonable as the word choice of dreams tends to be more abstract. Lastly, percentile scores of PCSYNp and PCNARp show that the majority of models are producing poems that are both syntactically simplistic and narrative. Most PCREFp scores are on the lower end of the scale. We suspect the reason these scores are lower is because the poems are not necessarily related and were all input at once.