Abstract

The amount of information in the scientific literature of the bio-medical domain is growing exponentially, which makes it difficult in developing a smart medical system. Summarization techniques help for efficient searching and understanding of relevant information from the medical documents. In the paper, an evolutionary algorithm based ensemble extractive summarization technique is devised as a smart medical application with the idea of hybrid artificial intelligence on natural language processing. We have considered the abstracts of the target article and its cited articles as the base summaries and a multi-objective evolutionary algorithm is applied for generating the ensemble summary of the target article. Each sentence of the base summaries is represented by a concept vector of the medical terms contained in it with the help of the Unified Modelling Language System (UMLS) tool which is widely used in various smart medical applications. These terms carry the key information of the sentence which is very useful to find out the semantic similarity among the sentences. Fitness functions of the evolutionary algorithm are mainly defined using clustering coefficient and sparsity index, the concepts of graph theory. After the convergence of the algorithm, the best solution of the final population gives the ensemble summary. Next, the semantic similarity of each sentence in the target article with the ensemble summary is calculated and the sentences which are most similar to the ensemble summary are considered as the summary of the target article. The method is applied to the articles available in the PubMed MEDLINE database system and experimental results are compared with some state of the art methods applied in the Bio-medical domain. Experimental results and comparative study based on the performance evaluation show that the method competes with some recently proposed summarization methods and outperforms others, which express the effectiveness of the proposed methodology. Different statistical tests have also been made to observe that the method is statistically significant.

Graphic abstract

中文翻译：

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基于进化算法的集成提取总结开发智能医疗系统

摘要

生物医学领域的科学文献中的信息量呈指数级增长，这使得开发智能医疗系统变得困难。摘要技术有助于有效搜索和理解医学文档中的相关信息。在本文中，基于自然语言处理的混合人工智能思想，设计了一种基于进化算法的集成提取摘要技术作为智能医疗应用。我们将目标文章及其引用文章的摘要视为基础摘要，并应用多目标进化算法来生成目标文章的整体摘要。借助广泛用于各种智能医疗应用的统一建模语言系统（UMLS）工具，基本摘要的每个句子都由包含在其中的医学术语的概念向量表示。这些词条携带了句子的关键信息，对于找出句子之间的语义相似度非常有用。进化算法的适应度函数主要使用聚类系数和稀疏指数，图论的概念来定义。算法收敛后，最终种群的最佳解给出集成总结。接下来，计算目标文章中每个句子与集成摘要的语义相似度，将与集成摘要最相似的句子视为目标文章的摘要。该方法应用于 PubMed MEDLINE 数据库系统中可用的文章，并将实验结果与应用于生物医学领域的一些最先进的方法进行比较。基于性能评估的实验结果和比较研究表明，该方法与一些最近提出的摘要方法竞争并优于其他方法，这表明了所提出方法的有效性。还进行了不同的统计测试以观察该方法具有统计显着性。基于性能评估的实验结果和比较研究表明，该方法与一些最近提出的摘要方法竞争并优于其他方法，这表明了所提出方法的有效性。还进行了不同的统计测试以观察该方法具有统计显着性。基于性能评估的实验结果和比较研究表明，该方法与一些最近提出的摘要方法竞争并优于其他方法，这表明了所提出方法的有效性。还进行了不同的统计测试以观察该方法具有统计显着性。

图形摘要