Statistical language modelling for automatic story generation

This paper proposes an end-to-end Natural Language Generation approach to automatically create fiction stories using statistical language models. The proposed approach integrates the stages of macroplanning and the surface realisation, necessary to determine the content to write about together with the structure of the story, and the syntactic and lexical realisation of sentences to be generated, respectively. Moreover, the use of language models within the stages allows the generation task to be more flexible, as far as the adaptation of the approach to different languages, domains and textual genres is concerned. In order to validate our approach, two evaluations were performed. On the one hand, the influence of integrating position-specific language modelling in the macroplanning stage into the surface realisation module was evaluated. On the other hand, a user evaluation was performed to analyse the generated stories in a qualitative manner. Although there is still room for improvement, the results obtained from the first evaluation in conjunction with the user evaluation feedback shows that the combination of the aforementioned stages in an end-to-end approach is appropriate and have positive effects in the resulting generated text.