Summary

This chapter dealt with text summarization, yet another hot topic in NLP. Systems of this kind aim to reduce the information load imposed by the overabundance of online text data. We used various extractive and abstractive text summarization techniques to deliver accurate summaries.

The first part of the chapter focused on web crawling and scraping, where you became acquainted with the basic concepts, the relevant technologies, and how to implement web spiders in Python. The provided coding examples constitute a sufficient guide to implementing your web crawlers for different tasks.

Next, we discussed various topics that led to the comprehension of the transformer model. For example, we debated why having a single context vector between the encoder and the decoder is a bottleneck. We also discussed attention mechanisms that enhance some parts of the input data while diminishing others. Finally, utilizing a corpus of Wikipedia pages, we created a dataset and trained the...