Abstract
Sentence compression is known as presenting a sentence in a fewer number of words compared to its original one without changing the meaning. Recent works on sentence compression formulates the problem as an integer linear programming problem (ILP) then solves it using an external ILP-solver which suffers from slow running time. In this article, the sentence compression task is formulated as a two-class classification problem and used a gradient boosting technique to solve the problem. Different models are created using two different datasets. The best model is taken for evaluation. The quality of compression is measured using two important quality measures, informativeness and compression rate. This article has achieved 70.2 percent in informativeness and 38.62 percent in compression rate.
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