Natural Language Generation (NLG) is a subfield of Natural Language Processing (NLP) that focuses on automatically generating human-like text.

With the rapid advancement of AI technologies, NLG has become an important tool for various industries, including finance, healthcare, and media, to name a few.

In this article, we will discuss what you need to know about NLG, its key concepts, applications, and the factors that affect its quality.

What is NLG, and how does it work?

NLG uses AI techniques such as machine learning and rule-based systems to produce text. The input to an NLG system can be structured or unstructured data or a combination of both.

Structured data refers to information organized in a pre-defined format, such as a spreadsheet or database. Unstructured data refers to information not organized in a pre-defined format, such as text or images.

The output text can be used for various purposes, such as summarizing information, generating reports, creating content, etc.

What are the applications of NLG?

Text generation

One of the key benefits of NLG is its ability to automate the process of generating reports, summaries, and other forms of text.

It can save time and resources for organizations and improve the accuracy and consistency of the generated text.

For example, in finance, NLG can be used to generate financial reports, summaries, and investment recommendations. In healthcare, it can be used to generate patient reports, medical summaries, and treatment plans.

Supporting text styles and tones

NLG systems can also generate texts in different styles and tones, depending on the intended audience and purpose.

For example, an NLG system designed for a news website can generate articles in a neutral tone while one designed for a marketing campaign can generate text in a more persuasive tone.

This flexibility makes NLG a versatile tool that can be adapted to a wide range of applications.

Evaluation of texts

Another important aspect of NLG is the evaluation of the generated text. It involves comparing the generated text to human-written text to assess its quality and similarity.

There are several metrics used for this evaluation, such as BLEU, ROUGE, and METEOR, which measure the similarity of the generated text to the reference text. The results of these evaluations can be used to improve the models and algorithms used in NLG.

Limitations of the NLG

However, the quality of NLG depends on several factors, including the data used, the models used, and the evaluation metrics used.

The data used for training and testing the NLG system must be high quality and representative of the target domain. The models must be well-designed and optimized for the specific task at hand. The evaluation metrics used must be appropriate and reflect the goals of the NLG system. It is also important to note that NLG is still a developing field, and there is still much room for improvement.

For example, current NLG systems struggle with understanding the context and meaning of the input data, leading to errors or inaccuracies in the generated text.

However, with continued research and development, these limitations are likely to be overcome, and NLG will become an even more powerful tool.

Conclusion

All in all, Natural Language Generation is a fascinating and rapidly growing field that has the potential to revolutionize the way we generate text.

Its benefits are numerous, and its applications are diverse, making it a valuable tool for organizations looking to automate the process of generating text.

However, to achieve high-quality results, it is critical to carefully consider the data, models, and evaluation metrics used, as well as to continue to push the boundaries of what is possible with NLG.