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Language-independent extractive automatic text summarization based on automatic keyword extraction
Computer Speech & Language ( IF 4.3 ) Pub Date : 2021-07-27 , DOI: 10.1016/j.csl.2021.101267
Ángel Hernández-Castañeda 1, 2 , René Arnulfo García-Hernández 2 , Yulia Ledeneva 2 , Christian Eduardo Millán-Hernández 2
Affiliation  

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.



中文翻译:

基于自动关键词提取的语言无关提取自动文本摘要

本研究提出了一种用于自动提取文本摘要 (EATS) 任务的语言和领域独立方法,该方法基于遗传算法 (GA) 支持的聚类方案,以找到句子的最佳分组。此外,我们的方法包括一个主题建模算法,用于根据自动生成的关键字在集群中找到关键句子。我们的实验结果表明,我们的系统通过两个通用步骤的应用优于以前的方法:聚类,有助于增加覆盖率,以及向模型添加语义信息,有助于检测聚类中的关键句子并提高精度.

更新日期:2021-08-01
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