Addressing Challenges of Modern News Agencies via Predictive Modeling, Deep Learning, and Transfer Learning

Today's news agencies are moving from traditional journalism, where publishing just a few news articles per day was sufficient, to modern content generation mechanisms, which create more than thousands of news pieces every day.

With the growth of these modern news agencies comes the arduous task of properly handling this massive amount of data that is generated for each news article.

Therefore, news agencies are constantly seeking solutions to facilitate and automate some of the tasks that have been previously done by humans.

In this dissertation, we focus on some of these problems and provide solutions for two broad problems which help a news agency to not only have a wider view of the behaviour of readers around the article but also to provide an automated tools to ease the job of editors in summarizing news articles.

These two disjoint problems are aiming at improving the users' reading experience by helping the content generator to monitor and focus on poorly performing content while allow them to promote the good-performing ones.

We first focus on the task of popularity prediction of news articles via a combination of regression, classification, and clustering models.

We next focus on the problem of generating automated text summaries for a long news article using deep learning models.

The first problem aims at helping the content developer in understanding of how a news article is performing over the long run while the second problem provides automated tools for the content developers to generate summaries for each news article.