基于差分进化聚类的文本摘要方法

Albaraa Abuobieda, N. Salim, M. Binwahlan, A. H. Osman
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引用次数: 13

摘要

本文采用三种相似度量;采用归一化谷歌距离(NGD)、Jaccard和余弦相似度度量对基于文本的聚类问题进行了测试。采用差分进化算法优化数据聚类过程,提高生成文本摘要的质量。本研究以注册下召回导向评估(ROUGE)作为评估工具来评估摘要的品质。实验结果表明,我们提出的所有方法都优于基准方法。更重要的是,基于jaccard相似性的方法优于本研究中提出的所有其他方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Differential evolution cluster-based text summarization methods
In this paper, three similarity measures; Normalized Google Distance (NGD), Jaccard and Cosine Similarity measures were employed and tested for textual based clustering problem. A robust evolutionary algorithm called Differential Evolution algorithm was also used to optimize the data clustering process and increase the quality of the generated text summaries. The Recall Oriented Under Gisting Evaluation (ROUGE) was used as an evaluation measure toolkit to assess the quality of the summaries. Experimental results showed that all of our proposed methods outperformed the benchmark methods. More importantly, the Jaccard-similarity based method surpassed all the other proposed methods in this study.
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