Text extraction from documents using NLP or Deep Learning I am looking for references(Papers/github projects) on how to use deep learning in a text extraction task.

Recently I was given a task to extract important information from documents of similar type, say for example legal merger documents. I have thousands of legal merger documents as inputs. A paralegal would go through the entire document and highlight important points from the document. This is the extracted text. 

What I want to do: Given a document(say legal merger document) I want to use DL or NLP  to extract the information from the legal document that would be similar to that of the information extracted by paralegal.

I am currently using bag of words model to extract text from the document, calculating sentiment and displaying the sentences with positive or negative sentiments. This yielded very bad results.

My knowledge in DL/NLP is very limited and I am particularly looking for some interesting papers and github projects related to text extraction using these frameworks. Can anyone please provide me with some references and suggestions on how to tackle this issue?
Jurafsky and Martin's NLP textbook has a chapter about information extraction that should be a good starting point. For example, if you want to extract company names it will tell you how to do that.


  A paralegal would go through the entire document and highlight important points from the document. 


What you need to do depends heavily on what your definition of "important" is here. It would help if you can give some specific examples.