I'm using the entire text book data by scraping the information of each chapter.
How do I highlight the spacy spancat NER or Bert Q&A based models to train multiple comma separated values in the text as important. For each chapter this behavior is recurring so how do I train the model to detect that it is important and that section is the important part which discusses different topics for each chapter.
Eg: After scraping the chapter 1: There is 1 paragraph that describes what topics will be covered in this chapter like x,y,z,a,b,c,d,e.
Similarly in chapter 2, There is 1 paragraph that describes what topics will be covered in this chapter like f,g,h,i,j,k.
How do I train this model in such a way that if I move to next chapter or even take the next book, It'll recognize these patters as the topics in that chapter or get all important topics discussed in the entire book? SO, it'll be the sum of all such comma separated values in the book.