Narrative Graph: Telling Evolving Stories Based on Event-centric Temporal Knowledge Graph

Abstract

As the main channel for people to obtain information and express their opinions, online media generate a huge amount of unstructured news documents every day and make it difficult for people to perceive major societal events and grasp the evolution of events. Previous studies on storyline generation are generally based on document clustering without considering event arguments and relations between events. Event-centric knowledge graph has been used to facilitate the construction of news documents to form structured event representation. Although some studies have attempted to construct timelines based on event-centric knowledge graphs, it is difficult for timelines to depict the complex structures of event evolution. In this paper, we try to represent news documents as an event-centric knowledge graph, and compress the whole knowledge graph into salient complex events in temporal order to generate storylines named narrative graph. We first collect news documents from news platforms, construct an event ontology, and build an event-centric knowledge graph with temporal relations. Graph neural network is used to detect events, while BERT fine-tuning is leveraged to identify temporal relations between events. Then, a novel generation framework of narrative graph with constraints of coherence and coverage is proposed. In addition, a case study is implemented to demonstrate how to utilize narrative graph to analyze real-world event. The experiment results show that our approach significantly outperforms the baseline approaches.