Single Sentence Compression Using SVM
Presenting a sentence in less number of words compared to its original one without changing the meaning is known as sentence compression. Most recent works on sentence compression models define the problem as an integer linear programming problem and solve it using an external ILP solver which suffers from slow running time. In this paper, we have presented a machine learning approach to single sentence compression. The sentence compression task is modeled as a two class classification problem and used support vector machine to solve the problem. Different learning models are created using different types of kernel functions. Finally, it has been observed that RBF kernel gives good result compared to other kernel functions for this compression task of single sentence.