We suggest a new NLG task in the context of the discourse generation pipeline of computational storytelling systems. This task, textual embellishment, is defined by taking a text as input and generating a semantically equivalent output with increased lexical and syntactic complexity. Ideally, this would allow the authors of computational storytellers to implement just lightweight NLG systems and use a domain-independent embellishment module to translate its output into more literary text. We present promising first results on this task using LSTM Encoder-Decoder networks trained on the WikiLarge dataset. Furthermore, we introduce "Compiled Computer Tales", a corpus of computationally generated stories, that can be used to test the capabilities of embellishment algorithms.
@cite_14: Narrative generation has historically suffered from poor writing quality, stemming from a narrow focus on story grammars and plot design. Moreover, to-date natural language generation systems have not been capable of faithfully reproducing either the variety or complexity of naturally occurring narratives. In this article we first propose a model of narrative derived from work in narratology and grounded in observed linguistic phenomena. Next we describe the AUTHOR architecture for narrative generation and an end-to-end implementation of the AUTHOR model in the STORYBOOK narrative prose generation system. Finally, we present a formal evaluation of the narratives that STORYBOOK produces.
Traditionally, narrative generation systems like were devised to generate macro-level plot and character elements without connecting them into a comprehensive story in natural language. Later work has sought to combine plot and NLG into a unified system @cite_14 . These full pipelines generate story elements and employ rule-based language models that produce naturally sounding narratives. While these approaches make use of text embellishment, the disadvantage of these rule-based approaches is that rules for text enrichment have to be conceived of in advance by the system's architect. The work presented here, in contrast, seeks to learn a natural language representation from human data to automatically embellish priorly generated narratives.