Background Linking of News Articles

Abstract

Nowadays, it is very rare to find a single news article that solely contains all the information about a certain subject or event. Very recently, a number of methods were proposed to find background articles that can be linked to a query article to help readers understand its context, whenever they are reading it. These methods, however, are still far from reaching an optimal performance. In my thesis, I propose techniques that aim to improve the background linking process for online news articles. For example, I propose to exploit different techniques to construct representative search queries from the query article, that be can effectively employed to retrieve the required background links in an ad-hoc setting. Moreover, I aim to study how to train neural models that can learn the background relevance between pairs of articles. Through the proposed techniques, I aim to experiment with the possible criteria that may distinguish useful background articles from non-relevant ones, such as their semantic and lexical similarities, and the granularity of the topics discussed in each. Defining these criteria will enable understanding the notion of background relevance, and accordingly allow for effective background links retrieval.