  Key Point Analysis(KPA) is a relatively new task in NLP that combines
summarization and classification by extracting argumentative key points (KPs)
for a topic from a collection of texts and categorizing their closeness to the
different arguments. In our work, we focus on the legal domain and develop
methods that identify and extract KPs from premises derived from texts of
judgments. The first method is an adaptation to an existing state-of-the-art
method, and the two others are new methods that we developed from scratch. We
present our methods and examples of their outputs, as well a comparison between
them. The full evaluation of our results is done in the matching task -- match
between the generated KPs to arguments (premises).
