We have learned about concepts from natural language processing, text classification, text summarization, and the application of deep learning CNN models in text domain. We have seen that transfer learning in terms of word embedding is the default first step in most of the use cases, especially if we have less training data. We have seen how to apply transfer learning for the text CNN model, learned on a huge Amazon product review dataset, to make predictions on a small movie review dataset, a related but not the same domain.

Also, we have learned here how we can use the learned CNN model for other text processing tasks, such as the summarization and representation of documents as dense vectors, which can be used in an information retrieval system to improve the retrieval performance.