摘要评估和文本分类

K. Ahmad, B. Vrusias, P. C. F. D. Oliveira
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引用次数: 14

摘要

一般来说,对摘要的评价取决于它与原文主要观点的接近程度。这就产生了一个问题,即源文本中的要点是什么,以及如何使用这些信息本身来识别源文本。当我们讨论摘要的自动评估时,这是至关重要的。所以要点的问题是原文。通常,这将围绕着关键字的核心。然而,文本的显著性、频率以及与集合中其他文本的关系(这些关键词可能是)是重要的。使用神经网络的文本分类很好地说明了这些问题,并且具有实际意义。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Summary evaluation and text categorization
In general terms the evaluation of a summary depends on how close it is to the chief points in the source text. This begets the question as to what are the chief points in the source text and how is this information used in itself in identifying the source text. This is crucially important when we discuss automatic evaluation of summaries. So the question of main points is the source text. Typically, this would be around a nucleus of keywords. However, the salience, the frequency, and the relationship of the text with other texts in the collection (of these keywords is perhaps) are important. Text categorisation using neural networks explicates these points well and also has a practical impact.
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