文本摘要研究进展与趋势

doi:10.11959/j.issn.2096-109x.2018048

Abstract

Abstract:

With the explosive growth of information on the Internet,how to extract useful information from massive information has become a key technical issue.The text summarization technology can compress and extract refined and concise document information from big data,effectively reducing the user information overload problem,and it has become a research hotspot.The domestic and foreign text summarization methods and their concrete realization in recent years were analyzed,the advantages and disadvantages between traditional methods and deep learning summary methods were compared,and a reasonable outlook for future research directions was made.

Key words: big data, text summarization, machine learning, traditional methods, deep learning

CLC Number:

TP393

Tuosiyu MING, Hongchang CHEN. Research progress and trend of text summarization[J]. Chinese Journal of Network and Information Security, 2018, 4(6): 1-10.

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方法	优点	缺点
基于统计学方法	依据文本形式上的规律，简单直观，避免考虑复杂的句法、语法结构，易于实现且应用广泛，无需训练数据，执行速度快	只是单纯利用了单词表层特征，没有充分挖掘词义关系和语义特征，存在较大局限性
基于外部语义资源方法	在统计学方法的基础上利用词间关系、词义关系进行了改进，使文本摘要的语义性能得到了一定的提高	受收录词汇的限制比较大；对于文章题目依赖程度较高；分词对关键词的影响较大；相似度阈值的选取对构建词汇链有影响。语法语义结构不连贯
基于图排序方法	适用于结构较为松散且涉及主题较多的结构；计算句子权重的同时可以充分考虑词汇之间、词组之间或句子之间的全局关系；无监督，语言独立，不需要对大量语料进行处理	通常只考虑了句子节点间的相似性关系，而忽略了文档篇章结构以及句子上下文的信息；相似度计算的好坏决定了关键词和句子重要性排序的正确与否；对数据的利用不够充分；没有考虑信息冗余
基于统计机器学习方法	特征选择和训练分类器的选择上有较大的可供选择范围，还可以综合一些开放性特征提高分类的精度	需要人工标注的数据集；效果严格依赖于训练数据质量的好坏；监督或半监督，执行速度较无监督的方法慢
基于深度学习方法	降低了对人工的依赖，可以高效地进行训练；可以与多种神经网络结构和Sequence-to-Sequence模型结合，生成文本摘要的可读性和准确度高	可解释性差；需要大量人工标注的数据集；由于有复杂的神经网络结构的引入，执行速度慢，需要花费相对较长的时间；对计算机性能有一定的要求

Research progress and trend of text summarization

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