基于布谷鸟搜索优化算法的多文档摘要方法

为最大化生成摘要的信息量,提出一种基于布谷鸟搜索(CS)算法与多目标函数的多文档摘要方法。对多文档数据进行预处理,通过句子分割、分词、移除停用词和词干化将文档转化为词语的基本处理形式,计算经数据预处理后的句子信息量得分并将其作为CS算法的输入,再基于多目标函数生成包含原始文档重要信息的句子以组成最终的摘要。实验结果表明,与基于粒子群优化算法和双层K最近邻算法的多文档摘要方法相比,该方法在最大化生成摘要信息量的前提下,保证了高可读性和低冗余性,并且在DUC基准数据集上的摘要平均准确度高达0.99。

Multiple Document Summarization Method Based on Optimized Cuckoo Search Algorithm

To maximize the amount of information of generated summary,this paper proposes a multiple document summarization method based on the Cuckoo Search(CS)algorithm and multiple objective function.The method pre-processes data of multiple documents by using sentence segmentation,word segmentation,removal of stop words and word drying to transform the documents into a basic processed form of words.Then the score of information amount of pre-processed sentences is calculated to serve as the input of the CS algorithm.Based on the multiple objective function,the sentences including key information of original texts are generated to form the ultimate summarization.Results show that compared with multiple document summarization methods based on Particle Swarm Optimization(PSO)algorithm and Double-layer K Nearest Neighbor(DKNN)algorithm,the proposed summarization method maximizes the amount of information in the generated summary while keeping high readability and low redundancy.Its average accuracy rate on the DUC benchmark dataset reaches 0.99.