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

TL;DR

EventNarrative is a large-scale, high-quality dataset linking event-centric knowledge graphs with natural language text, designed to advance research in graph-to-text generation and evaluation.

Contribution

The paper introduces a novel, large-scale event-centric dataset with rich ontology and high data quality, filling a gap in graph-to-text research and enabling better model evaluation.

Findings

EventNarrative contains approximately 230,000 graph-text pairs.

The dataset is six times larger than existing parallel datasets.

Baseline evaluations demonstrate the dataset's utility for model development.

Abstract

We introduce EventNarrative, a knowledge graph-to-text dataset from publicly available open-world knowledge graphs. Given the recent advances in event-driven Information Extraction (IE), and that prior research on graph-to-text only focused on entity-driven KGs, this paper focuses on event-centric data. However, our data generation system can still be adapted to other other types of KG data. Existing large-scale datasets in the graph-to-text area are non-parallel, meaning there is a large disconnect between the KGs and text. The datasets that have a paired KG and text, are small scale and manually generated or generated without a rich ontology, making the corresponding graphs sparse. Furthermore, these datasets contain many unlinked entities between their KG and text pairs. EventNarrative consists of approximately 230,000 graphs and their corresponding natural language text, 6 times larger than the current largest parallel dataset. It makes use of a rich ontology, all of the KGs entities are linked to the text, and our manual annotations confirm a high data quality. Our aim is two-fold: help break new ground in event-centric research where data is lacking, and to give researchers a well-defined, large-scale dataset in order to better evaluate existing and future knowledge graph-to-text models. We also evaluate two types of baseline on EventNarrative: a graph-to-text specific model and two state-of-the-art language models, which previous work has shown to be adaptable to the knowledge graph-to-text domain.