Unsupervised latent event representation learning and storyline extraction from news articles based on neural networks

Abstract

Storyline extraction aims to generate concise summaries of related events unfolding over time from a collection of temporally-ordered news articles. Some existing approaches to storyline extraction are typically built on probabilistic graphical models that jointly model the extraction of events and the storylines from news published in different periods. However, their parameter inference procedures are often complex and require a long time to converge, which hinders their use in practical applications. More recently, a neural network-based approach has been proposed to tackle such limitations. However, event representations of documents, which are important for the quality of the generated storylines, are not learned. In this paper, we propose a novel unsupervised neural network-based approach to extract latent events and link patterns of storylines jointly from documents over time. Specifically, event representations are learned by a stacked autoencoder and clustered for event extraction, then a fusion component is incorporated to link the related events across consecutive periods for storyline extraction. The proposed model has been evaluated on three news corpora and the experimental results show that it outperforms state-of-the-art approaches with significant improvements.