A supervised approach for text illustration

Abstract

In this paper we propose a novel method to illustrate text articles with pictures from a tagged repository. Certain types of documents, like news articles, are often accompanied by a few pictures only. Prior works leverage topics or key phrases from the text to suggest relevant pictures. We propose a supervised model based on features like readability, picturability, sentiment polarity, and presence of important phrases, to identify and rank key sentences. The proposed method then suggests some relevant pictures based on the top ranked sentences thus identified. © 2016 ACM.