RNN based question answer generation and ranking for financial documents using financial NER.

Organizations, governments and many entities deal with an expanse of voluminous financial documents and this necessitates a need for a financial expert system which, given a financial document, extracts finance-related questions and answers from it. This expert system helps us to adequately summarize the document in the form of a question-answer report. This paper introduces the novel idea of generating finance-related questions and answers from financial documents by introducing a custom Financial Named Entity Recognizer, which can identify financial entities in a document with an accuracy of 92%. We have introduced a method of generating finance-based questions using a sample document to obtain a set of generalized questions that we can feed to any similar financial document. We also record the expected answer type during the question generation phase, which helps to develop a robust mechanism to verify that we always generate the correct answers during the answer extraction stage. [ABSTRACT FROM AUTHOR]