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An Empirical Analysis of Similarity based Single Document Summarization

机译:基于相似性的单一文件摘要的实证分析

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In current days, there is an enormous amount of information available in various resources like www, e-books, tweets, several articles, and social media posts. Human beings have very little time available to them, whereas they must obtain the necessary information from these resources due to public and business requirements. Text Summarization is one of the solutions to this problem. It is a process of shortening the length of text present in the document computationally and permit to obtain the most vital information in a very less amount of time. Therefore, a similarity-based framework is suggested to summarize a document concerning various similarity measures. Document Understanding Conference (DUC) dataset is considered for empirical validation of the model.
机译:在目前的几天中,各种资源都有大量信息,如www,电子书,推文,几篇文章和社交媒体帖子。 人类对他们来说有很少的时间,而他们必须由于公共和业务要求获得这些资源的必要信息。 文本摘要是此问题的解决方案之一。 它是在计算上缩短文档中存在的文本长度的过程,并允许在更少的时间内获得最重要的信息。 因此,建议基于相似性的框架总结了有关各种相似性措施的文件。 文档了解会议(DUC)数据集被认为是模型的实证验证。

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