Can Knowledge Graphs Simplify Text?

TL;DR

KGSimple introduces an unsupervised method leveraging knowledge graphs to simplify text by constructing concise, meaningful representations, improving over existing models that start from complex text directly.

Contribution

It presents a novel unsupervised approach that uses knowledge graph techniques to generate simplified text from KG inputs, enhancing text simplification methods.

Findings

Effective in simplifying text while preserving meaning

Outperforms existing unsupervised models on KG-to-text datasets

Demonstrates the potential of KGs in text simplification tasks

Abstract

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.