CDDS: Constraint-driven document summarization models

► We propose constraint-driven document summarization models. ► These models with tuning the constraint parameters can drive content coverage and diversity in a summary. ► We adopt a discrete PSO algorithm to solve the optimization problem. ► Experimental study on DUC2005 and DUC2007 datasets shows that the proposed models outperform other methods. This paper proposes a constraint-driven document summarization approach emphasizing the following two requirements: (1) diversity in summarization, which seeks to reduce redundancy among sentences in the summary and (2) sufficient coverage, which focuses on avoiding the loss of the document’s main information when generating the summary. The constraint-driven document summarization models with tuning the constraint parameters can drive content coverage and diversity in a summary. The models are formulated as a quadratic integer programming (QIP) problem. To solve the QIP problem we used a discrete PSO algorithm. The models are implemented on multi-document summarization task. The comparative results showed that the proposed models outperform other methods on DUC2005 and DUC2007 datasets.