Automatic Summary Extraction in Texts Using Genetic Algorithms
Automatic Summary Extraction in Texts Using Genetic Algorithms
复制标题
使用遗传算法自动提取文本摘要
DOI：
10.1109/siu49456.2020.9302205
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发表时间：
2020
期刊：
影响因子：
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通讯作者：
Ahmet Cahit Yaşa
中科院分区：
文献类型：
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作者：
Abdullah Ammar Karcioglu;Ahmet Cahit Yaşa
文本自动文摘是自然语言处理领域研究已久的应用之一。网络资源中信息量的增加增加了对自动文本摘要方法的需求。很难设计一个系统来产生由人手创作的摘要。正因为如此，许多研究者都把注意力集中在句子或段落的抽取上，这是一种摘要。在这项研究中，我们介绍了一种使用遗传算法创建的方法来生成此类摘要。在对文本进行预处理后，创建词汇并将其作为输入输入到所提出的方法。采用基于遗传算法的选句方法进行摘要，生成摘要后，使用适应度函数对摘要进行评价。在我们的第一个模型中，适应度函数基于每个单词的频率和单词对的频率。在基于TF-IDF的另一种方法中，使用相同的数据集讨论了应用模型的结果，包括查准率、召回率、fcore和Rouge度量。
Automatic text summarization is one of the applications of natural language processing that has been studied for a long time. The increase in the amount of information in web resources has increased the need for automatic text summarization methods. It is difficult to design a system to produce abstracts created by human hands. For this reason, many researchers have focused on extracting sentences or paragraphs, which is a kind of summary. In this study, we introduce a method that was created using genetic algorithms to generate such summaries. After the texts are preprocessed, vocabulary is created and given as input to the proposed method. The sentence selection based on Genetic Algorithm is used to summarize and after that the summary is created, it is evaluated using the fitness function. In our first model, the fitness function is based on the frequency of each word and the word pair frequencies. The results of the applied model are discussed using the same dataset in another method based on tf-idf, with precision, recall, fscore and Rouge metrics.