Can Large Language Models Support Editors Pick Related News Articles?

Abstract

Editors and journalists play an important role on news platforms. Besides creating trustworthy news stories, they also provide valuable expertise on which
stories are placed on the front page and hand-pick related articles for platform
users to read further. This paper focuses on the specific task of related article selection commonly carried out daily by editors and journalists on news platforms.
This is typically a manual process that utilizes an internal search tool to first
find a pool of potential candidate articles. Then, from those candidate articles,
editors and journalists hand-pick the top related articles for a given news article
as a form of expert-selected suggestions for the readers. Although this task can
be an important part of the editorial process in news platforms, it may become
time-consuming and demanding, often requiring significant human effort.
In addressing this challenge, we propose an automatic mechanism to support
editors and journalists in this task by incorporating one of the latest Large
Language Models (LLMs), i.e., GPT4o-mini, to shortlist a set of related articles
and recommend them to be checked by journalists and editors. Our evaluation
of the proposed approach, based on a real-world dataset from one of the largest
commercial Norwegian news platforms (i.e., TV 2), demonstrates the effectiveness
of the approach in supporting editors and journalists in their task of selecting
relevant news articles.