A classic problem in natural-language generation (NLG) involves taking structured data, such as a table, as input, and producing text that adequately and fluently describes this data as output. Unlike machine translation, which aims for complete transduction of the sentence to be translated, this form of NLG is usually taken to require addressing (at least) two separate challenges: what to say, the selection of an appropriate subset of the input data to discuss, and how to say it, the surface realization of a generation.
a high-level explanation of the dataset characteristics We introduce WikiOFGraph, a novel large-scale, domain-diverse dataset synthesized by LLMs, ensuring superior graph-text consistency to advance general-domain graph-to-text generation. * explain motivations and summary of its content The scarcity of high-quality, general-domain G2T generation datasets restricts progress in the generaldomain G2T generation research. To address this issue, we introduce Wikipedia OntologyFree Graph-text dataset (WikiOFGraph), a new large-scale G2T dataset generated using a novel method that leverages Large Language Model (LLM) and Data-QuestEval. * potential use cases of the dataset General Domain Knowledge Graph-to-Text generation