Aspect Learning for Multimedia Summarization via Nonparametric Bayesian

Summarization is desirable for efficient comprehension of an increasingly vast amount of data. A summary of multiple documents is a concise description of the main topic. Generally speaking, a topic delivers various aspects. For example, the natural disaster topic is likely to imply the aspects of casualties and rescue. Therefore, a good summary is expected to cover all the informative aspects of a topic in order to enhance both diversity and coverage of the topic. However, for the real-world data, the profile of aspects in a given topic (e.g., the number of the aspects as well as their appropriate describing sentences or images) is hardly specified in advance. To address this problem, this paper proposes an approach to learn the hidden aspects in the topics via a nonparametric Bayesian model for multimedia summarization, namely, aspect learning for multimedia summarization via nonparametric Bayesian (ALSNB). More specifically, we introduce the priors of beta-Bernoulli process and Dirichlet process into the traditional dictionary learning. As a result, the proposed approach is able to adaptively identify the particular aspects of an individual topic. The experimental results on several datasets for text summarization and image summarization show the superiority of the proposed ALSNB over other methods.