The term content automation has many facets. Basically, content automation subsumes all processes related to the automated creation, organisation and distribution of content. In practice, this includes a number of sometimes quite different applications.
On the one hand, content automation can mean the internal management of a constantly growing amount of content, for example the classification of incoming e-mails. On the other hand, the automation of content is also the analysis and classification of texts, for example in order to curate website content for users in a targeted manner.
In German-speaking countries, many experts associate content automation with the automated creation of editorial content. Synonyms and related terms for this type of content automation include automatic text generation, automatic text creation, robotic journalism, and natural language generation.
A special form of content automation is becoming increasingly widespread in the media industry. Robot journalism relieves the editors of far-reaching news websites of unattractive, routine reporting and creates time and space for the creative tasks which constitute the core essence of journalism. Niche providers, such as regional news portals, can also expand their range of topics with automated texts. This in turn enables additional target groups to be addressed in the competition for content.
Even beyond online media, automatic text generation is already a fixed component of content strategies in many areas.
Content automation in the sense of automatic text creation is only possible where data is the basis of the content. In all of the applications described above, software based on artificial intelligence processes structured data into a natural language contribution using text templates and conditions. The more extensive and better structured the data and the more complex the pre-formulated conditions, the deeper and more varied the thematic content of texts can be.
Content automation also includes solutions that use natural language understanding to interpret content from a large volume of digital files so that they can be automatically rearranged in later steps. A practical example would be the bundling of news contributions from a news portal to relevant topic pages. First, all content is semantically enriched. In addition, fully-automated systems analyse demand on the internet and identify relevant topics at an early stage. Search-engine-optimized topic pages are provided by matching the semantically enriched content with the analysed demand on the net. Content automation thus contributes to an individualized user experience – without requiring any additional editorial resources.