Automatic headline generation has the potential to significantly assist editors charged with head-
lining articles. Approaches to automation in the headlining process can range from tools as creative
aids, to complete end to end automation. The latter is difficult to achieve as journalistic require-
ments imposed on headlines must be met with little room for error, with the requirements depending
on the news brand in question.
This thesis investigates automatic headline generation in the context of the Finnish newsroom. The
primary question I seek to answer is how well the current state of text generation using deep neural
language models can be applied to the headlining process in Finnish news media.
To answer this, I have implemented and pre-trained a Finnish generative language model based
on the Transformer architecture. I have fine-tuned this language model for headline generation
as autoregression of headlines conditioned on the article text. I have designed and implemented
a variation of the Diverse Beam Search algorithm, with additional parameters, to perform the
headline generation in order to generate a diverse set of headlines for a given text.
The evaluation of the generative capabilities of this system was done with real world usage in mind.
I asked domain-experts in headlining to evaluate a generated set of text-headline pairs. The task
was to accept or reject the individual headlines in key criteria. The responses of this survey were
then quantitatively and qualitatively analyzed.
Based on the analysis and feedback, this model can already be useful as a creative aid in the
newsroom despite being far from ready for automation. I have identified concrete improvement
directions based on the most common types of errors, and this provides interesting future work.
