Sheng Xu
ABSTRACT
In medical insurance industry, a lot of human labor is required to collect information of claimants. Human assessors need to converse with claimants in order to record key information and organize it into a structured summary. With the purpose of helping save human labor, we propose the task of conversation-oriented structured summarization which aims to automatically produce the desired structured summary from a conversation automatically. One major challenge of the task is that the structured summary contains multiple fields of different types. To tackle this problem, we propose a unified approach COSSUM based on prompting to generate the values of all fields simultaneously. By learning all fields together, our approach can capture the inherent relationship between them. Moreover, we propose a specially designed curriculum learning strategy for model training. Both automatic and human evaluations are performed, and the results show the effectiveness of our proposed approach.
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Index Terms
- COSSUM: Towards Conversation-Oriented Structured Summarization for Automatic Medical Insurance Assessment
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