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.

我们提出了KGSimple，这是一种新颖的无监督文本简化方法，通过利用已建立的知识图谱技术构建简化的知识图谱路径，并生成保留原始输入意义的简洁文本。我们的模型通过迭代和抽样建立的知识图谱方法有能力从知识图谱起始进行文本简化，同时利用KG-to-text生成流畅且描述性强的句子输出。我们在目前可用的KG-to-text数据集上评估了KGSimple模型的各种设置，证明了与从给定复杂文本开始的无监督文本简化模型相比的有效性。我们的代码可以在GitHub上获得。

知识图谱是否能简化文本？