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Language-independent extractive automatic text summarization based on automatic keyword extraction

机译:基于自动关键字提取的语言独立的提取自动文本摘要

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摘要

This study proposes a language and domain independent approach for automatic extractive text summarization (EATS) tasks, which is based on a clustering scheme supported by a genetic algorithm (GA), to find an optimal grouping of sentences. Furthermore, our approach includes a topic modeling algorithm to find the key sentences in clusters based on automatically generated keywords. Our experimental results show that our system outperforms previous methods through the application of two general steps: clustering, which helps to increase coverage, and the addition of semantic information to the model, which facilitates the detection of the key sentences in the clusters and improves precision.
机译:本研究提出了一种语言和域的自动提取文本摘要(饮食)任务的语言和域名,其基于遗传算法(GA)支持的聚类方案,以找到最佳的句子分组。 此外,我们的方法包括一个主题建模算法,用于基于自动生成的关键字找到群集中的密钥句。 我们的实验结果表明,我们的系统通过应用两个一般步骤:群集有助于增加覆盖的群集,以及向模型添加语义信息的群集,这有助于检测群集中的密钥句并提高精度并提高精度 。

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