Related Article Extraction Using Learning to Rank

Related articles of a news article help readers to access other beneficial information efficiently. Thus, it is an important task for newspaper companies to link appropriate related articles to a news article on news websites. However, it costs to select related articles from enormous news articles that have been published in the past. To solve the issue, we introduce “learning to rank” methods to the related article extraction task to support business operation. We propose some ranking methods and report the accuracy of each method with three evaluation metrics. We also show some input-output examples by the method that scores the highest accuracy in our experiments.