  Knowledge Graph (KG)-to-Text Generation has seen recent improvements in
generating fluent and informative sentences which describe a given KG. As KGs
are widespread across multiple domains and contain important entity-relation
information, and as text simplification aims to reduce the complexity of a text
while preserving the meaning of the original text, we propose KGSimple, a novel
approach to unsupervised text simplification which infuses KG-established
techniques in order to construct a simplified KG path and generate a concise
text which preserves the original input's meaning. Through an iterative and
sampling KG-first approach, our model is capable of simplifying text when
starting from a KG by learning to keep important information while harnessing
KG-to-text generation to output fluent and descriptive sentences. We evaluate
various settings of the KGSimple model on currently-available KG-to-text
datasets, demonstrating its effectiveness compared to unsupervised text
simplification models which start with a given complex text. Our code is
available on GitHub.
