集成粒子群优化与追溯搜索优化功能提取与二维卷积神经网络和基于注意力的堆叠双向长短期记忆分类器,以有效的单个和多文档总结
Jyotirmayee Rautaray1, Sangram Panigrahi2, Ajit Kumar Nayak2
1Department of Computer Science and Engineering, Siksha O Anusandhan University Institute of Technical Education and Research, Bhubaneswar, Odisha, India.
PeerJ. Computer science
|February 3, 2025
概括
本研究介绍了一种改进的文本总结模型,使用粒子群集优化与追溯搜索优化 (PSOBSA) 和使用ABS-BiLSTM的2D CNN. 这种新方法显著提高了多文档总结的准确性和连贯性,与现有的方法相比.
科学领域:
- 自然语言处理自然语言处理.
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 数字信息的扩散需要有效的方法来从大型文档集中提取关键见解.
- 文本总结,特别是多文档总结 (MDS),在准确和连贯地综合信息方面存在重大挑战.
- 现有的自动文本总结技术,包括深度学习和进化算法,看起来很有前途,但对于复杂的总结任务需要进一步改进.
研究的目的:
- 开发一种先进的文本摘要模型,能够从单个和多个文档中生成精确和连贯的摘要.
- 简要介绍一个即兴的粒子集群优化与追溯搜索优化 (PSOBSA) 算法,以增强功能提取.
- 将一个二维卷积神经网络 (2D CNN) 与基于注意力的堆叠双向长期短期记忆 (ABS-BiLSTM) 集成,用于句子分析和总结生成.
主要方法:
- 使用拟议的即兴粒子优化与追溯搜索优化 (PSOBSA) 算法进行特征提取.
- 使用二维卷积神经网络 (2D CNN) 和基于注意力的堆叠双向长期短期记忆 (ABS-BiLSTM) 模型进行分类和句子分析.
- 在基准数据集 (DUC 2002,2003,2005,Multi-News,CNN/Daily Mail) 上使用ROUGE,BLEU,凝聚力和可读性等指标进行性能评估.
主要成果:
- 与多种先进的总结技术 (PSO,CSO,LSTM-CNN,SVR,BSA,ACO,FFA) 相比,提议的PSOBSA增强的2D CNN与ABS-BiLSTM模型表现出更高的性能.
- 该模型在红色,蓝色,凝聚力和可读性方面获得了更高的分数,这表明总结质量有所改善.
- 实验结果证实了该模型在生成连贯,非冗余和语法正确的概要时的有效性,无论是单文档概要 (SDS) 还是多文档概要 (MDS).
结论:
- 集成的PSOBSA,2D CNN和ABS-BiLSTM模型代表了自动文本总结的重大进步.
- 提出的方法有效地解决了多文档总结的复杂性,提供了更好的准确性和连贯性.
- 这项研究为未来自然语言处理和信息检索系统的发展提供了坚实的框架.
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