Sentence compression is an important problem in natural language processing with wide applications in text summarization, search engine and human–AI interaction system etc. In this paper, we design a hybrid extractive sentence compression model combining a probability language model and a parse tree language model for compressing sentences by guaranteeing the syntax correctness of the compression results. Our compression model is formulated as an integer linear programming problem, which can be rewritten as a difference-of-convex (DC) programming problem based on the exact penalty technique. We use a well-known efficient DC algorithm—DCA to handle the penalized problem for local optimal solutions. Then a hybrid global optimization algorithm combining DCA with a parallel branch-and-bound framework, namely PDCABB, is used for finding global optimal solutions. Numerical results demonstrate that our sentence compression model can provide excellent compression results evaluated by F-score, and indicate that PDCABB is a promising algorithm for solving our sentence compression model.

句子压缩是自然语言处理中的一个重要问题，在文本摘要，搜索引擎和人机交互系统等方面具有广泛的应用。本文设计了一种混合提取句子压缩模型，该模型结合了概率语言模型和解析树语言模型通过保证压缩结果的语法正确性来压缩句子。我们的压缩模​​型被公式化为整数线性规划问题，可以基于精确惩罚技术将其重写为凸差（DC）编程问题。我们使用众所周知的高效DC算法DCA来处理局部最优解的不利问题。然后，将DCA与并行分支和边界框架PDCABB相结合的混合全局优化算法，用于查找全局最优解。数值结果表明，我们的句子压缩模型可以提供出色的F分数评估压缩结果，并表明PDCABB是解决句子压缩模型的有前途的算法。