Matching news articles and wikipedia tables for news augmentation

Nowadays, digital-news understanding is often overwhelmed by the deluge of online information. One approach to cover this gap is to outline the news story by highlighting the most relevant facts. For example, recent studies summarize news articles by generating representative headlines. In this paper, we go beyond and argue news understanding can also be enhanced by surfacing contextual data relevant to the article, such as structured web tables. Specifically, our goal is to match news articles and web tables for news augmentation. For that, we introduce a novel BERT-based attention model to compute this matching degree. Through an extensive experimental evaluation over Wikipedia tables, we compare the performance of our model with standard IR techniques, document/sentence encoders and neural IR models for this task. The overall results point out our model outperforms all baselines at different levels of accuracy and in the mean reciprocal ranking measure.