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近期取得了进展。由于知识图谱跨多个领域广泛应用，且包含重要的实体-关系信息，同时文本简化旨在降低文本复杂度并保留原义，我们提出KGSimple——一种融合知识图谱技术的无监督文本简化新方法，通过构建简化后的知识图谱路径并生成保留原始输入语义的简洁文本。模型采用迭代采样的知识图谱优先策略，在从知识图谱出发时，通过学习保留关键信息并利用KG-to-Text生成技术输出流畅描述性句子，从而实现文本简化。我们基于现有KG-to-Text数据集对KGSimple模型的多种设置进行了评估，结果显示其相较于从给定复杂文本出发的无监督文本简化模型更具有效性。相关代码已发布于GitHub。