问将长串裁剪成包含完整句子的段落

EN

import nltk

# this line only needs to be run once per environment:
nltk.download('punkt') 

text = """The Stanford NLP Group makes some of our Natural Language Processing software available to everyone! We provide statistical NLP, deep learning NLP, and rule-based NLP tools for major computational linguistics problems, which can be incorporated into applications with human language technology needs.These packages are widely used in industry, academia, and government. This code is actively being developed, and we try to answer questions and fix bugs on a best-effort basis. All our supported software distributions are written in Java. Current versions of our software from October 2014 forward require Java 8+. (Versions from March 2013 to September 2014 required Java 1.6+; versions from 2005 to Feb 2013 required Java 1.5+. The Stanford Parser was first written in Java 1.1.) Distribution packages include components for command-line invocation, jar files, a Java API, and source code. You can also find us on GitHub and Maven. A number of helpful people have extended our work, with bindings or translations for other languages. As a result, much of this software can also easily be used from Python (or Jython), Ruby, Perl, Javascript, F#, and other .NET and JVM languages."""

sents = nltk.sent_tokenize(text)

sents
# outputs:
['The Stanford NLP Group makes some of our Natural Language Processing software available to everyone!',
 'We provide statistical NLP, deep learning NLP, and rule-based NLP tools for major computational linguistics problems, which can be incorporated into applications with human language technology needs.These packages are widely used in industry, academia, and government.',
 'This code is actively being developed, and we try to answer questions and fix bugs on a best-effort basis.',
 'All our supported software distributions are written in Java.',
 'Current versions of our software from October 2014 forward require Java 8+.',
 '(Versions from March 2013 to September 2014 required Java 1.6+; versions from 2005 to Feb 2013 required Java 1.5+.',
 'The Stanford Parser was first written in Java 1.1.)',
 'Distribution packages include components for command-line invocation, jar files, a Java API, and source code.',
 'You can also find us on GitHub and Maven.',
 'A number of helpful people have extended our work, with bindings or translations for other languages.',
 'As a result, much of this software can also easily be used from Python (or Jython), Ruby, Perl, Javascript, F#, and other .NET and JVM languages.']

def g(sents):
    idx = 0
    text_length = 0
    for i, s in enumerate(sents):
        if text_length + len(s) > 300:
            yield ' '.join(sents[idx:i])
            text_length = len(s)
            idx = i
        else:
            text_length += len(s)
    yield ' '.join(sents[idx:])

for s in g(sents):
    print(s)
outputs:
The Stanford NLP Group makes some of our Natural Language Processing software available to everyone!
We provide statistical NLP, deep learning NLP, and rule-based NLP tools for major computational linguistics problems, which can be incorporated into applications with human language technology needs.These packages are widely used in industry, academia, and government.
This code is actively being developed, and we try to answer questions and fix bugs on a best-effort basis. All our supported software distributions are written in Java. Current versions of our software from October 2014 forward require Java 8+.
(Versions from March 2013 to September 2014 required Java 1.6+; versions from 2005 to Feb 2013 required Java 1.5+. The Stanford Parser was first written in Java 1.1.) Distribution packages include components for command-line invocation, jar files, a Java API, and source code.
You can also find us on GitHub and Maven. A number of helpful people have extended our work, with bindings or translations for other languages. As a result, much of this software can also easily be used from Python (or Jython), Ruby, Perl, Javascript, F#, and other .NET and JVM languages.

[len(s) for s in g(sents)]
#outputs:
[100, 268, 244, 276, 289]