Trends in Extractive and Abstractive Techniques in Text Summarization
Trends in Extractive and Abstractive Techniques in Text Summarization
复制标题
DOI：
10.5120/20559-2947
复制
发表时间：
2015-05
期刊：
影响因子：
--
通讯作者：
Neelima Bhatia;Arunima Jaiswal
中科院分区：
文献类型：
--
作者：
Neelima Bhatia;Arunima Jaiswal
事实证明，文本摘要比人工汇总大数据更具优势。它通过保留内容来浓缩文本的显著特征，并服务于有意义的总结。分类可以通过两种方式进行--摘要和抽象摘要。摘要摘要使用统计和语言特征来确定重要特征，并将它们融合为较短的版本。而抽象摘要理解整个文档，然后生成摘要。本文给出了抽取方法和抽象方法的框架。关键词摘要，抽象摘要
Text Summarization was proved to be an advantage over manually summarizing the large data. It condenses the salient features from the text by preserving the content and serves the meaningful summary. Classification can be done in two ways - extractive and abstractive summarization. Extractive summarization uses statistical and linguistic features to determine the important features and fuse them into a shorter version. Whereas abstractive summarization understands the whole document and then generates the summary. In this paper extractive and abstractive methods are framed. Keywordssummarization, Abstractive summarization