This project aims to generate meaningful and high-quality text by combining multiple sources. The procedure used to achieve this result is called Text Combining.

In this project, we explore different techniques that are related to NLP and Text Mining. This project has many use cases, such as improving and optimizing the inferences of other text processing operations. For instance, generating text tasks such as abstractive summarization, translation, or automatic text/article generation.

Moreover, this project can also be used to correct the inferences of Optical Character Recognition and Speech-To-Text to optimize the efficiency of Artificial Intelligence APIs. This solution also helps mitigate problems related to noise present in images and audio recordings, which can be useful for improving a set of faulty texts, such as historical texts, corrupted, or poorly translated ones.