Storyline extraction from news articles with dynamic dependency
Linsen Guo, Deyu Zhou, Yulan He, Haiyang Xu · Intelligent Data Analysis · 2020
Storyline generation aims to produce a concise summary of related events unfolding over time from a collection of news articles. It can be cast into an evolutionary clustering problem by separating news articles into different epochs. Existing unsupervised approaches to storyline generation are typ ically based on probabilistic graphical models. They assume that the storyline distribution at the current epoch depends on the weighted combination of storyline distributions in the latest previous M epochs. The evolutionary parameters of such long-term dependency are typically set by a fixed exponential decay function to capture the intuition that events in more recent epochs have stronger influence to the storyline generation in the current epoch. However, we argue that the amount of relevant historical contextual information should vary for different storylines. Therefore, in this paper, we propose a new Dynamic Dependency Storyline Extraction Model (D2SEM) in which the dependencies among events in different epochs but belonging to the same storyline are dynamically updated to track the time-varying distributions of storylines over time. The proposed model has been evaluated on three news corpora and the experimental results show that it outperforms the state-of-the-art approaches and is able to capture the dependency on historical contextual information dynamically.