Keyword-based Augmentation Method to Enhance Abstractive Summarization for Legal Documents

Since state-of-the-art machine learning models like Transformers can not handle long text well, the quality of the summarization of legal documents is still not desirable. In order to improve the ability of machine learning models to understand the context of a long document, we introduce the keywords into the models to guide the summarization to locate and capture key information from long documents such as legal cases. Different from other works leveraging keywords to enhance the model, we further investigate how keyword quality impacts summarization. To improve the performance of the summarization, we also explore different methods for effectively encoding exceptionally lengthy documents and models for keyword extraction. The experiment results demonstrated that the keywords-based augmentation method is effective for summarization and higher-quality keywords can enhance the summarization models.