Improving The Processing Of Question Answer Based Legal Documents

In the legal domain, documents of various types are created in connection with a case. Some are transcripts prepared by court reporters, based on notes taken during the proceedings of a trial or deposition. For example, deposition transcripts capture the conversations between attorneys and deponents. These documents are mostly in the form of question-answer (QA) pairs. Summarizing the information contained in these documents is a challenge for attorneys and paralegals because of their length and form. Having automated methods to convert a QA pair into a canonical form could aid with the extraction of insights from depositions. These insights could be in the form of a short summary, a list of key facts, a set of answers to specific questions, or a similar result from text processing of these documents. In this paper, we describe methods using NLP and Deep Learning techniques to transform such QA pairs into a canonical form. The resulting transformed documents can be used for summarization and other downstream tasks.