Saurabh Chakravarty, Maanav Mehrotra, Edward Fox
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.
Saurabh Chakravarty, Maanav Mehrotra, Raja Venkata Satya Phanindra Chava, Han Liu, Matthew Krivansky, Edward A. Fox: Improving the Processing of Question Answer Based Legal Documents. JURIX 2019: 13-22
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- Legal Knowledge and Information Systems: JURIX
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