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首页> 外文期刊>American journal of applied sciences >MODELLING KNOWLEDGE SUMMARIZATION BY EVOLVING FUZZY GRAMMAR
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MODELLING KNOWLEDGE SUMMARIZATION BY EVOLVING FUZZY GRAMMAR

机译：进化模糊语法对知识摘要的建模

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摘要

Summarized text is a simplified and condensed version of the original text containing highlighted information to help the audience get the gist in a short period of time. Typically, text summarization produces abstract or a paragraph-like outputs by omitting details and irrelevant information. However, the text summary can also be produced in a visualized form, such as a chart, graph or table representing a collection of similar cases. The visualized version generates a statistical-like presentation, which often involves numerical and ordinal observation of the gathered knowledge from the text. This requires lexical syntactic understanding of the text. Essential to achieve this goal is topic identification, message analysis/interpretation and knowledge summarization generation. The objective of this study is to model knowledge summarization problem using the evolving fuzzy grammar technique and we focus on metadata generation for producing visualized knowledge summarization. The process comprises of: (ⅰ) identifying the underlying structure of the texts for knowledge summarization, (ⅱ) represent the identified knowledge for summarization manipulation and (ⅲ) presentation of the summarized knowledge. A prototype called FTCat© is developed as a proof of concept and we demonstrate its practicality in summarizing news reports.
机译：摘要文本是原始文本的简化和精简版本，其中包含突出显示的信息，以帮助受众在短时间内掌握要点。通常，文本摘要会通过省略细节和不相关的信息来产生抽象或类似段落的输出。但是，文本摘要也可以以可视化形式生成，例如代表相似案例集合的图表，图形或表格。可视化版本生成类似统计的表示形式，通常涉及从文本中对收集的知识进行数字和有序观察。这需要对文本的词汇句法理解。实现此目标的关键是主题识别，消息分析/解释和知识摘要生成。这项研究的目的是使用演化的模糊语法技术对知识汇总问题进行建模，并且我们专注于元数据生成以产生可视化的知识汇总。该过程包括：（ⅰ）识别文本的基本结构以进行知识汇总，（ⅱ）代表所标识的知识以进行汇总操作，以及（ⅲ）展示所总结的知识。开发了一个称为FTCat©的原型作为概念证明，我们在总结新闻报道时证明了它的实用性。

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