Can Knowledge Graphs Simplify Text?

• URL: http://arxiv.org/abs/2308.06975v3
• Date: Wed, 25 Oct 2023 00:57:54 GMT
• Title: Can Knowledge Graphs Simplify Text?
• Authors: Anthony Colas, Haodi Ma, Xuanli He, Yang Bai, Daisy Zhe Wang
• Abstract summary: KGSimple is a novel approach to unsupervised text simplification. Our model is capable of simplifying text when starting from a KG by learning to keep important information. We evaluate various settings of the KGSimple model on currently-available KG-to-text datasets.
• Score: 17.642947840076577
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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