# Sentence Compression via DC Programming Approach

## TL;DR

This paper introduces a novel sentence compression model combining probability and parse tree models, formulated as an ILP, and solves it efficiently using a DC programming approach with guarantees of syntax correctness and meaning preservation.

## Contribution

The paper presents a new ILP-based sentence compression model and a DC programming approach with a parallel-branch-and-bound framework for optimal solutions.

## Key findings

- Model guarantees syntax correctness and meaning retention.
- Proposed algorithm achieves high-quality compression.
- Numerical results show effective performance.

## Abstract

Sentence compression is an important problem in natural language processing. In this paper, we firstly establish a new sentence compression model based on the probability model and the parse tree model. Our sentence compression model is equivalent to an integer linear program (ILP) which can both guarantee the syntax correctness of the compression and save the main meaning. We propose using a DC (Difference of convex) programming approach (DCA) for finding local optimal solution of our model. Combing DCA with a parallel-branch-and-bound framework, we can find global optimal solution. Numerical results demonstrate the good quality of our sentence compression model and the excellent performance of our proposed solution algorithm.