Formulation of document summarization as a 0–1 nonlinear programming problem

We proposed a novel text summarization model based on 0–1 non-linear programming problem. This proposed model covers the main content of the given document(s) through sentence assignment. We implemented our model on multi-document summarization task. When comparing our method to several existing summarization methods on an open DUC2001 and DUC2002 datasets, we found that the proposed method could improve the summarization results significantly. The methods were evaluated using ROUGE-1, ROUGE-2 and ROUGE-W metrics.

► We model text summarization as a nonlinear 0–1 programming problem.
► This model balances content coverage and diversity in the summary.
► We utilize the DPSO-EDA algorithm to solve the optimization problem.
► Experiments show that our model produces very competitive results.