With software automatically producing texts in natural language from structured data, the evolution of natural language generation (NLG) is changing traditional news production. The paper first addresses the question whether NLG is able to perform the functions of professional journalism on a technical level. A technological potential analysis therefore uncovers the technological limitations and possibilities of NLG, accompanied by an institutional classification following Weischenberg, Malik, and Scholl. Overall, NLG is explained within the framework of algorithmic selection and along its technological functionality. The second part of the paper focuses on the economic potential of NLG in journalism as well as indicating its institutionalization on an organizational level. Thirteen semi-structured interviews with representatives of the most relevant service providers detail the current market situation. Following Heuss, the development of the NLG market is classified into phases. In summary, although the market for NLG in journalism is still at an early stage of market expansion, with only a few providers and journalistic products available, NLG is able to perform tasks of professional journalism at a technical level. The analysis therefore sets the basis to analyze upcoming challenges for journalism research at the intersection of technology and big data.
The availability of data feeds, the demand for news on digital devices, and advances in algorithms are helping to make automated journalism more prevalent. This article extends the literature on the subject by analysing professional journalists' experiences with, and attitudes to, the technology. Uniquely, the participants were drawn from a range of news organizations-including the BBC, CNN, and Thomson Reuters-and had first-hand experience working with robo-writing software provided by one of the leading technology suppliers. The results reveal journalists' judgements on the limitations of automation, including the nature of its sources and the sensitivity of its "nose for news". Nonetheless, journalists believe that automated journalism will become more common, increasing the depth, breadth, specificity, and immediacy of information available. While some news organizations and consumers may benefit, such changes raise ethical and societal issues and, counter-intuitively perhaps, may increase the need for skills-news judgement, curiosity, and scepticism-that human journalists embody.
This article introduces an exploratory computational approach to extending the realm of automated journalism from simple descriptions to richer and more complex event driven narratives, based on original applied research in structured journalism. The practice of automated journalism is reviewed and a major constraint on the potential to automate journalistic writing is identified, namely the absence of data models sufficient to encode the journalistic knowledge necessary for automatically writing eventdriven narratives. A detailed proposal addressing this constraint is presented, based on the representation of journalistic knowledge as structured event and structured narrative data. We describe a prototyped database of structured events and narratives, and introduce two methods of using event and narrative data from the prototyped database to provide journalistic knowledge to a commercial automated writing platform. Detailed examples of the use of each method are provided, including a successful application of the approach to stories about car chases, from initial data reporting through to automatically generated text. A framework for evaluating automatically generated event-driven narratives is proposed, several technical and editorial challenges to applying the approach in practice are discussed, and several high-level conclusions about the importance of data structures in automated journalism workflows are provided.
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