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？

Can Knowledge Graphs Simplify Text?

Recent improvements in KG-to-text generation are due to additional auxiliary
pre-training tasks designed to give the fine-tune task a boost in performance.
These tasks require extensive computational resources while only suggesting
marginal improvements. Here, we demonstrate that by fusing graph-aware elements
into existing pre-trained language models, we are able to outperform
state-of-the-art models and close the gap imposed by additional pre-training
tasks. We do so by proposing a mask structure to capture neighborhood
information and a novel type encoder that adds a bias to the graph-attention
weights depending on the connection type. Experiments on two KG-to-text
benchmark datasets show our models are competitive while involving fewer
parameters and no additional pre-training tasks. By formulating the problem as
a framework, we can interchange the various proposed components and begin
interpreting KG-to-text generative models based on the topological and type
information found in a graph.

通过将图感知元素融合到现有的预训练语言模型中，提出了一种掩蔽结构来捕获邻域信息和一种新型类型编码器，以便于基于图中的拓扑和类型信息解释 KG-to-text 生成模型（在不需要额外的预训练任务的情况下），实验结果表明我们的模型具有较高的竞争力。