You are provided with the abstract of a scientific paper and the abstracts of the papers it references. Your task is to write a Related Work section for the main paper. This section should concisely summarize and integrate the key points from the cited abstracts, reflecting how they relate to the main paper's contributions and context. In your summary, use numerical citation markers like [1], [2], etc., to refer to the corresponding papers.

Main Abstract:
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.

Cited Abstract(s):
[1]: 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 [1] . 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.