摘要翻译：
快速摘要是自动文档摘要器的创新实现，它以英语输入文档并评估每个句子。扫描器或评估器根据其语法结构和在段落中的位置确定标准。程序然后要求用户指定该人希望突出显示的句子数。例如，如果用户要求有三个最重要的句子，它会用绿色突出显示第一个最重要的句子。通常这是包含结论的句子。然后快速总结找到第二个最重要的句子，通常称为卫星，并用黄色突出它。这通常是主题句。然后程序找到第三个最重要的句子，并用红色突出显示。这种技术的实现在一个信息过载的社会中很有用，当一个人通常每天收到42封电子邮件时（微软）。这篇论文也是对机器学习在纹理翻译中遇到的困难的一个坦率的看法。然而，它谈到了如何克服历史上阻碍进展的障碍。本文提出了数学元数据准则来证明句子的重要性。正如生物信息学中研究关系对称性的工具一样，该工具寻求以更清晰的方式对词进行分类。“调查发现，工人平均每周只有三天工作时间。”微软新闻中心。微软。网。2012年3月31日。
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英文标题：
《Quick Summary》
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作者：
Robert Wahlstedt
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最新提交年份：
2012
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分类信息：

一级分类：Computer Science        计算机科学
二级分类：Computation and Language        计算与语言
分类描述：Covers natural language processing. Roughly includes material in ACM Subject Class I.2.7. Note that work on artificial languages (programming languages, logics, formal systems) that does not explicitly address natural-language issues broadly construed (natural-language processing, computational linguistics, speech, text retrieval, etc.) is not appropriate for this area.
涵盖自然语言处理。大致包括ACM科目I.2.7类的材料。请注意，人工语言（编程语言、逻辑学、形式系统）的工作，如果没有明确地解决广义的自然语言问题（自然语言处理、计算语言学、语音、文本检索等），就不适合这个领域。
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一级分类：Computer Science        计算机科学
二级分类：Artificial Intelligence        人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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英文摘要：
  Quick Summary is an innovate implementation of an automatic document summarizer that inputs a document in the English language and evaluates each sentence. The scanner or evaluator determines criteria based on its grammatical structure and place in the paragraph. The program then asks the user to specify the number of sentences the person wishes to highlight. For example should the user ask to have three of the most important sentences, it would highlight the first and most important sentence in green. Commonly this is the sentence containing the conclusion. Then Quick Summary finds the second most important sentence usually called a satellite and highlights it in yellow. This is usually the topic sentence. Then the program finds the third most important sentence and highlights it in red. The implementations of this technology are useful in a society of information overload when a person typically receives 42 emails a day (Microsoft). The paper also is a candid look at difficulty that machine learning has in textural translating. However, it speaks on how to overcome the obstacles that historically prevented progress. This paper proposes mathematical meta-data criteria that justify the place of importance of a sentence. Just as tools for the study of relational symmetry in bio-informatics, this tool seeks to classify words with greater clarity. "Survey Finds Workers Average Only Three Productive Days per Week." Microsoft News Center. Microsoft. Web. 31 Mar. 2012.
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PDF链接：
https://arxiv.org/pdf/1210.3634