We started off the chapter by understanding what text summarization is. We learned that there are two types of text summarization tasks – one is extractive summarization and the other is abstractive summarization. In extractive summarization, we create a summary from a given text by just extracting only the important sentences. Unlike extractive summarization, in abstractive summarization, we will not create a summary by just extracting important sentences from the given text. Instead, in this type, we create a summary by paraphrasing the given text.
Next, we learned how to fine-tune BERT to perform the summarization task. We learned how BERTSUM works and how it used for summarization tasks. After understanding BERTSUM, we learned how to use BERTSUM with a classifier, with a transformer, and with LSTM for an extractive summarization task.
Later, we learned how to perform abstractive summarization using BERTSUM. For abstractive summarization, we used a transformer architecture...
