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.
The research is funded by Natural Science Foundation of China (Grant No: 11601327) and by the Key Construction National “985” Program of China (Grant No: WF220426001).
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Notes
- 1.
Punctuation is also deemed as word.
- 2.
\(\left[ \!\left[ m,n\right] \!\right] \) with \(m\le n\) stands for the set of integers between m and n.
- 3.
A function \(f:\mathbb {R}^n\rightarrow \mathbb {R}\) is called DC if there exist two convex functions g and h (called DC components) such that \(f=g-h\).
- 4.
The compression rate is computed by the length of compression over the length of original sentence.
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Niu, YS., Hu, XW., You, Y., Benammour, F.M., Zhang, H. (2020). Sentence Compression via DC Programming Approach. In: Le Thi, H., Le, H., Pham Dinh, T. (eds) Optimization of Complex Systems: Theory, Models, Algorithms and Applications. WCGO 2019. Advances in Intelligent Systems and Computing, vol 991. Springer, Cham. https://doi.org/10.1007/978-3-030-21803-4_35
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