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We introduce EventNarrative, a knowledge graph-to-text dataset from publiclyavailable open-world knowledge graphs. Given the recent advances inevent-driven Information Extraction (IE), and that prior research ongraph-to-text only focused on entity-driven KGs, this paper focuses onevent-centric data. However, our data generation system can still be adapted toother other types of KG data. Existing large-scale datasets in thegraph-to-text area are non-parallel, meaning there is a large disconnectbetween the KGs and text. The datasets that have a paired KG and text, aresmall scale and manually generated or generated without a rich ontology, makingthe corresponding graphs sparse. Furthermore, these datasets contain manyunlinked entities between their KG and text pairs. EventNarrative consists ofapproximately 230,000 graphs and their corresponding natural language text, 6times larger than the current largest parallel dataset. It makes use of a richontology, all of the KGs entities are linked to the text, and our manualannotations confirm a high data quality. Our aim is two-fold: help break newground in event-centric research where data is lacking, and to give researchersa well-defined, large-scale dataset in order to better evaluate existing andfuture knowledge graph-to-text models. We also evaluate two types of baselineon EventNarrative: a graph-to-text specific model and two state-of-the-artlanguage models, which previous work has shown to be adaptable to the knowledgegraph-to-text domain.

EventNarrative: A large-scale Event-centric Dataset for Knowledge Graph-to-Text Generation