Recommending Summarized News Articles on Social Media Platforms

Abstract

As social media platforms with short and easily digestible content gain popularity, some news consumers struggle with the vast amount of content and the inappropriate amount of time they need to consume it. With news recommender systems and automatic text summarization, one could help readers find news content faster and make it easier and quicker to consume the news. In this work, we wanted to find out if summarized articles could really enhance the experience of consuming news articles and if, with the help of our recommender system and the summarized news articles, readers could consume the articles in a shorter amount of time. We performed a user study on the participants’ perceived recommendation quality recommender system, the utility of the summary in news consumption, and preference of either recommended summarized news with the option to read the full article or recommended news with full articles and measuring the time they needed for each format. We found that most users are satisfied with the recommendation quality, prefer the summarized format, and find the summary useful for news consumption. Further, the participants not only perceived their reading time as quicker, but they consumed five news articles also, on average, 3.4 minutes quicker. Based on the results, by combining news recommender systems with automatic text summarization methods, the news consumption experience can be enhanced.