At Axel Springer, Europe’s largest digital publishing house, we own a lot of news articles from various media outlets such as Welt, Bild, Business Insider and many more. Arguably, the most important part of a news article is its title, and it is not surprising that journalists tend to spend a fair amount of their time to come up with a good one. For this reason, it was an interesting research question for us at Axel Springer AI whether we could create an NLP model that generates quality headlines from Welt news articles (see Figure 1). This could, for example, serve our journalists as inspiration for creating SEO titles, which our journalists often don’t have time for (in fact we’re working together with our colleagues from SPRING and AWS on creating a SEO title generator).
Figure 1: One example from our Welt.de headline generator.
Schäfer, Christian; Tran, Dat (2020): Headliner — Easy training and deployment of seq2seq models. Generating headlines from news articles using seq2seq models. Online verfügbar unter https://medium.com/axel-springer-tech/headliner-easy-training-and-deployment-of-seq2seq-models-2a26508b4dae, zuletzt geprüft am 27.11.2020.