August 2022
Intermediate to advanced
441 pages
11h 26m
Chinese
Content preview from Python文本分析
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文本摘要
｜
271
•
以结构化方式编写的长文本，包含多个段落，而且有很多页。例如案件处理、
研究论文、教材等。
•
短小的文本，比如新闻文章和博客等，其中可能包含图片、数据或其他图形元素。
•
多个短小的、对话形式的文本，可能包含表情符等特殊字符，而且没有良好的
结构。例如推文、论坛以及群聊应用程序。
每种类型的文本数据表示信息的方式都不同，因此用于生成某个文档摘要的方法可
能并不适合其他文档。在下面的案例中，我们将介绍处理这些类型的文本的方法，
还会提供选择正确方法的指南。
9.2.1
提取式摘要
所有提取方法都遵循以下三个基本的步骤：
1.
创建文本的中间表示。
2.
根据所选的表示，给句子
/
短语打分。
3.
给句子排名，然后选出句子来创建文本摘要。
虽然大多数解决方案都会遵循上述步骤，但每种方法创建中间表示或打分的方法各
有不同。
9.2.2
数据的预处理
在处理真实的案例之前，首先我们需要使用第
3
章介绍的解决方案，读取我们希望
总结的文本的
URL
。在这个案例中，我们将重点介绍文本的摘要生成，有关如何通
过
URL
提取数据的详细介绍，请参见第
3
章。为简洁起见，如下示例中的文本输出
经过了删减。如果想浏览整篇文章，请访问
URL
：
import reprlib
r = reprlib.Repr()
r.maxstring = 800
url1 = "https://www.reuters.com/article/us-qualcomm-m-a-broadcom-5g/\
what-is-5g-and-who-are-the-major-players-idUSKCN1GR1IN" ...
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.