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).
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如今，混合云平台作为打算实施私人和公共云应用组合的组织的有吸引力的解决方案，以满足其盈利能力。但是，这只能通过在加速执行过程时利用可用资源来实现。因此，部署新应用程序需要将其中一些流程致力于私有云解决方案，同时将其他过程分配给公共云。在此上下文中，设置本工作以帮助最小化相关成本，并在最小的执行时间内为最佳服务放置解决方案提供有效的选择。已经应用了几种进化算法来解决服务放置问题，并且在处理复杂的解决方案空间以提供最佳放置并经常产生短的执行时间。除了在处理服务放置问题方面发明细缺乏鲁棒性之外，还发现标准BPSO算法显示出显着的缺点，即容易捕获到本地Optima之外。因此，为了克服与标准BPSO相关的关键缺点，提出了增强的二进制粒子群优化（E-BPSO）算法，由粒子位置更新方程的修改组成，最初从连续PSO激发。我们所提出的E-BPSO算法显示在成本和执行时间方面以实际基准测试优越最先进的方法。
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