文本摘要的离散差分进化

Shweta Karwa, N. Chatterjee
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引用次数: 13

摘要

本文提出了一种改进的差分进化(DE)算法和优化准则函数,用于抽取文本摘要应用。基于针对文本摘要问题提出的准则函数,使用余弦相似度度量对相似句子进行聚类,并从每个聚类中选择重要句子生成文档摘要。改进的差分进化模型保证了状态值的整数化,因此与传统的DE方法相比,可以加快优化速度。实验表明,离散DE方法比传统DE方法在时间上提高了95.5%,而提取摘要的精度和召回率在所有情况下都保持相当。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Discrete Differential Evolution for Text Summarization
The paper proposes a modified version of Differential Evolution (DE) algorithm and optimization criterion function for extractive text summarization applications. Cosine Similarity measure has been used to cluster similar sentences based on a proposed criterion function designed for the text summarization problem, and important sentences from each cluster are selected to generate a summary of the document. The modified Differential Evolution model ensures integer state values and hence expedites the optimization as compared to conventional DE approach. Experiments showed a 95.5% improvement in time in the Discrete DE approach over the conventional DE approach, while the precision and recall of extracted summaries remained comparable in all cases.
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