Semantic Gated Network for Efficient News Representation

Learning an efficient news representation is a fundamental yet important problem for many tasks. Most existing news-relevant methods only take the textual information while abandoning the visual clues from the illustrations. We argue that the textual title and tags together with the visual illustrations form the main force of a piece of news and are more efficient to express the news content. In this paper, we develop a novel framework, namely Semantic Gated Network (SGN), to integrate the news title, tags and visual illustrations to obtain an efficient joint textual-visual feature for the news, by which we can directly measure the relevance between two pieces of news. Particularly, we first harvest the tag embeddings by the proposed self-supervised classification model. Besides, news title is fed into a sentence encoder pretrained by two semantically relevant news to learn efficient contextualized word vectors. Then the feature of the news title is extracted based on the learned vectors and we combine it with features of tags to obtain textual feature. Finally, we design a novel mechanism named semantic gate to adaptively fuse the textual feature and the image feature. Extensive experiments on benchmark dataset demonstrate the effectiveness of our approach.