Automatic Text Generation by Learning from Literary Structures
Angel Daza, Hiram Calvo, Jesus Figueroa-Nazuno · 2016
Most of the work dealing with automatic story production is based on a generic architecture for text generation; however, the resulting stories still lack a style that can be called literary.We believe that in order to generate automatically stories that could be compared with those by human authors, a specific methodology for fiction text generation should be defined.We also believe that it is essential for a story to convey the effect of originality to the person who is reading it.Our methodology proposes corpus-based generation of stories that could be called creative and also have a style similar to human fiction texts.We also show how these stories have plausible syntax and coherence, and are perceived as interesting by human evaluators.