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.

翻译：知识图谱到文本生成（KG-to-Text Generation）在生成描述给定知识图谱的流畅且信息丰富的句子方面取得了近期进展。鉴于知识图谱跨多个领域广泛存在，包含重要的实体-关系信息，而文本简化旨在降低文本复杂性的同时保留原文意义，我们提出KGSimple——一种新颖的无监督文本简化方法，该方法融合知识图谱成熟技术以构建简化后的知识图谱路径，并生成保留原始输入意义的简洁文本。通过迭代式且以知识图谱优先的采样策略，我们的模型能够从知识图谱出发完成文本简化：它学习保留关键信息，同时利用KG-to-Text生成技术输出流畅且描述性的句子。我们在现有KG-to-Text数据集上评估了KGSimple模型的多种设置，证明了其相较于从给定复杂文本开始的无监督文本简化模型具有更优效果。我们的代码已在GitHub上开源。