A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2019; you can also visit <a rel="external noopener" href="http://downloads.hindawi.com/journals/acisc/2011/351498.pdf">the original URL</a>. The file type is <code>application/pdf</code>.
<i title="Hindawi Limited">
<a target="_blank" rel="noopener" href="https://fatcat.wiki/container/fqn7ak2nbjc6xgzv2ya3jeam4m" style="color: black;">Applied Computational Intelligence and Soft Computing</a>
Extractive multidocument summarization is modeled as a modifiedp-median problem. The problem is formulated with taking into account four basic requirements, namely, relevance, information coverage, diversity, and length limit that should satisfy summaries. To solve the optimization problem a self-adaptive differential evolution algorithm is created. Differential evolution has been proven to be an efficient and robust algorithm for many real optimization problems. However, it still may converge<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1155/2011/351498">doi:10.1155/2011/351498</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/knxw7xqnjva2hcbnlucdxza7hq">fatcat:knxw7xqnjva2hcbnlucdxza7hq</a> </span>
more »... oward local optimum solutions, need to manually adjust the parameters, and finding the best values for the control parameters is a consuming task. In the paper is proposed a self-adaptive scaling factor in original DE to increase the exploration and exploitation ability. This paper has found that self-adaptive differential evolution can efficiently find the best solution in comparison with the canonical differential evolution. We implemented our model on multi-document summarization task. Experiments have shown that the proposed model is competitive on the DUC2006 dataset.
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20190505024958/http://downloads.hindawi.com/journals/acisc/2011/351498.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/5f/ef/5fef6c7893674835010a6e77681f3b5ec86ce46d.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1155/2011/351498"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> hindawi.com </button> </a>