A hybrid classification-based model for automatic text summarisation using machine learning approaches: CBS-ID3MV
作者: M. Esther Hannah
刊名: Int. J. of Product Development, 2019, Vol.23 (2/3), pp.201-211
来源数据库: Inderscience Enterprises Limited
DOI: 10.1504/IJPD.2019.099242
关键词: TrainingClassificationMachine learningDecision treesFeature extractionSummarisationRegression.
原始语种摘要: A hybrid approach for the generation of automatic text summarisation is achieved through CBS-ID3MV. A classification-based model using ID3 and multivariate (CBS-ID3MV) approach produces summaries from the text documents through classification and multiple linear regression. Efficient feature selection and extraction methods identify text features from each sentence, for the purpose of classifying summary sentences. The CBS-ID3MV model is trained with DUC 2002 training documents and the proposed approach's performance is measured at several compression rates namely 10%, 20% and 30% on the text data. The results got by the proposed framework works better when compared with other summarisers after evaluation using ROUGE metrics.
全文获取路径: Inderscience 出版公司 

  • automatic 自动的
  • machine 机器
  • feature 结构元件
  • learning 学识
  • model 模型
  • compression 压缩
  • documents 单据
  • classifying 分级
  • extraction 提取
  • metrics 规格