Digesting News Reader Comments via Fine-Grained Associations with Event Facets and News Contents

News articles from different sources reporting the same event are often associated with an enormous amount of reader comments resulting in difficulty in digesting the comments manually. Some of these comments, despite coming from different sources, discuss about a certain facet of the event. On the other hand, some comments discuss on the specific topic of the corresponding news article. We propose a framework that can digest reader comments automatically via fine-grained associations with event facets and news. We propose an unsupervised model called DRC, based on collective matrix factorization and develop a multiplicative-update method to infer the parameters. Experimental results show that our proposed DRC model can provide an effective way to digest news reader comments.