Text automation: Content creation using Natural Language Processing (NLP)

What was previously considered unfeasible in e-commerce, journalism and marketing has now become established project practice: automated text creation!

The areas of application for automatic text generation are highly diverse. They range from product texts in online shops to weather reports, stock market news and specialised news streams.

No longer is the term “robot journalism” being ridiculed, as the quality of automatically created product texts has improved to the level where they can no longer be distinguished from a manually written ones.

This has become possible through Natural Language Processing (NLP), which uses computer-based rules and algorithms to process natural language. NLP integrates methods and results from linguistics and enhances them with modern computer science and artificial intelligence. To achieve this, we also use AI technologies e.g. ChatGPT and machine learning.

Automated text generation solves quantity and time problems

Automatic text generation is usually the preferred method of choice for creating content if large amounts of text are involved, which are in certain patterns, such as horoscopes, TV programmes or product texts. For product managers and editors it is a tedious and time-consuming task to write hundreds or thousands of product descriptions in the same manner. The same applies, of course, to stock market and weather reports.

A text engine (“writing robot”) is indifferent to how much and what it writes. It can even generate text variations from data sets at the click of a mouse, which can be played out for search engine optimization (SEO) in different languages.

Often, similarly structured content has to be published regularly or quickly. This applies, for example, to sports results and news, but also to product range changes in an online fashion shop. Here, too, automatic content generation can create the respective texts in a matter of seconds.

Other use cases include changed technical constraints or new product-relevant safety regulations. The necessary adjustments can quickly be rolled out across all affected content using text engines.

Automatic text generation requires structured data

Structured data is an important prerequisite for automatic text generation. For product texts in e-commerce, these are often available in a product information system (PIM) or another database.

To enable text robots to generate meaningful sentences from structured product data, appropriate templates must be created first. To put it simply, these templates can be imagined as gap texts. The “gaps” in the text then correspond, for example, to product properties:

"This <clothing type> is particularly suitable for <occasion> in <colour>."

The product properties are then automatically filled with concrete values from the PIM by the text engine:

"This skirt is particularly suitable for festive occasions in white.”

Natural Language Processing (NLP) ensures that the automatically generated texts are grammatically correct – even in several languages. The use of synonyms and arbitrarily many sentence variations make the content sufficiently unique for both readers and search engines.

It is also possible to enrich the content with external data sources, e.g. weather forecasts for hotel descriptions or geodata for store locators. Automated text creation and structured data make it possible!

Automated content generation: text2net is the right partner for you

We work in partnership with leading NLP solution providers like AX Semantics and Retresco, but are basically vendor-neutral. Ultimately, it’s the customer requirements that decide which solution is to be used.

Do you already have well-structured data from which you want to create texts automatically? Perfect, because then we can create all required sentence templates according to your specifications in terms of tonality and wording, manage the synonyms, consider external data sources and also the language variants. The proper setup and configuration of these processes in the NLP software is a decisive success factor. Since 2004, we have specialised in working with complex technical content systems.

Your data is not yet (sufficiently) structured? Perhaps not even sufficiently digitalised? We can help, even if your data, for instance, is still “buried” in PDF documents that have not yet been tapped: With our semantic Data Extractor this content can also be read out automatically and converted into a structured form. This is an important step towards starting with text automation.

We support the entire content value chain, from content digitisation, data structuring, text automation and content enrichment up to delivering everything with our Smart Content Automation Services (SCAS).


For text automation, as with all our projects, we also handle important project management and control activities such as coordination with translation agencies if you so wish.

text2net is a Managed Service Provider of AX Semantics.

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