As correctly said in the previous answer, overfitting occurs when your model is not learning, but it is memorizing data. This leads in poor performances on the test set. If the training set is small the risk of overfitting is very high.
Data augmentation has the purpose to include variability in your dataset. I am more familiar with images, but the concept is the same. For each epoch you apply "some transformation" to your input data to prevent memorization. In the case of images, you can apply rotations or add noise. The informative content is not changed, but the model will not "see" the same image, it will not memorize, but you are aiding generalization.
I will use a metaphor. During school a common strategy is to memorize formulas and other stuffs to pass an exam. But if you do not learn the logic behind an equation, how to obtain a term instead of another one, you probably will fail the exam despite the fact that you have memorized everything.
Data augmentation is the equivalent of doing exercises on the same topic but changing each time the point of view in order to learn a global path and not memorize the information.