7. Text Generation and Summarization
This chapter begins with the concept of text generation using Markov chains, before moving on to two types of text summarization—namely, abstractive and extractive summarization. You will then explore the TextRank algorithm and use it with different datasets. By the end of this chapter, you will understand the applications and challenges of text generation and summarization using Natural Language Processing (NLP) approaches.
The ability to express thoughts in words (sentence generation), the ability to replace a piece of text with different but equivalent text (paraphrasing), and the ability to find the most important parts of a piece of text (summarization) are all key elements ...