Improving Deposition Summarization using Enhanced Generation and Extraction of Entities and Keywords

Abstract

In the legal domain, depositions help lawyers and paralegals to record details and recall relevant information relating to a case. Depositions are conversations between a lawyer and a deponent and are generally in Question-Answer (QA) format. These documents can be lengthy, which raises the need for applying summarization methods to the documents. Though many automatic summarization methods are available, not all of them give good results, especially in the legal domain. This creates a need to process the QA pairs and develop methods to help summarize the deposition. For further downstream tasks like summarization and insight generation, converting QA pairs to canonical or declarative form can be helpful. Since the transformed canonical sentences are not perfectly readable, we explore methods based on heuristics, language modeling, and deep learning, to improve the quality of sentences in terms of grammaticality, sentence correctness, and relevance. Further, extracting important entities and keywords from a deposition will help rank the candidate summary sentences and assist with extractive summarization. This work investigates techniques for enhanced generation of canonical sentences and extracting relevant entities and keywords to improve deposition summarization.