EPICURE - Aspect-based Multimodal Review Summarization

Restaurant reviews are popular and a valuable source of information. Often, large number of reviews are written for restaurants which warrants the need for automated summarization systems. In this paper we present epicure, a novel text and image summarization platform. For the summarization of opinionated content like reviews, considering different aspects have largely been ignored, and we address this by creating balanced reviews for different aspects like food and service. We argue that traditional criteria for extractive review summarization such as coverage and diversity have limited applicability. We draw on the power and usefulness of submodular functions for extractive summarization and introduce novel submodular functions such as importance, freshness, purity, trustworthiness and balanced opinion. We are also one of the first to provide an image summary for diffeerent aspects of a restaurant by mapping text to images using a multimodal neural network, for which we provide initial experiments. We show the effectiveness of our platform by evaluating it against strong baselines and also use crowdsourcing experiments for a subjective comparison of our approach with existing works.