Insurance Policy Summarization Using Natural Language Processing (NLP)

Abstract

 Policyholders may encounter challenges in understanding important terms and conditions of insurance policies due to their lengthiness and intricacy. This research investigates the usage of Natural Language Processing (NLP) methods for automating summaries of insurance policy documents. Our approach aims at producing concise and accurate summaries which highlights important aspects such as cover details, exclusions and premiums through a combination of extraction-based and abstractive summarization strategies. We elaborate on pre- processing procedures that consist of tokenization, part-of-speech tagging, text cleaning among others; moreover, we explain how modern deep learning models like transformers are employed for sentence creation and context understanding. To ensure that the summarized results are accurate and consistent, human evalu- ations were conducted in addition to standard metrics tests on the proposed sys- tem. Our findings demonstrate that NLP is able to enhance the understandability of insurance products thus facilitating better decision-making processes leading to higher levels of customer satisfaction.