> For the complete documentation index, see [llms.txt](https://codingling.gitbook.io/study/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://codingling.gitbook.io/study/basic-know/yu-chu-li/todo.md).

# Todo

文本摘要

假设摘要其实就是基于句子对读者的重要性和意义，对句子进行排序。

在通常情况下，有相对较多实体和名词的句子比其他句子更重要。

```python
import sys
f = open('nyt.txt', 'r')
news_content = f.read()

import nltk
results=[]
for sent_no,sentence in enumerate(nltk.sent_tokenize(news_content)):
    no_of_tokens=len(nltk.word_tokenize(sentence))
    # Let's do POS tagging
    tagged=nltk.pos_tag(nltk.word_tokenize(sentence))
    # Count the no of Nouns in the sentence
    no_of_nouns=len([word for word,pos in tagged if pos in ["NN","NNP"] ])
    #Use NER to tag the named entities.
    ners=nltk.ne_chunk(nltk.pos_tag(nltk.word_tokenize(sentence)), binary=False)
    no_of_ners= len([chunk for chunk in ners if hasattr(chunk, 'node')])
    score=(no_of_ners+no_of_nouns)/float(no_of_toekns)
    results.append((sent_no,no_of_tokens,no_of_ners,\
no_of_nouns,score,sentence))

for sent in sorted(results,key=lambda x: x[4],reverse=True):
    print sent[5]
```

使用TF-IDF

```python
>>>import nltk
>>>from sklearn.feature_extraction.text import TfidfVectorizer
>>>results=[]
>>>sentences=nltk.sent_tokenize(news_content)
>>>vectorizer = TfidfVectorizer(norm='l2',min_df=0, use_idf=True, smooth_idf=False, sublinear_tf=True)
>>>sklearn_binary=vectorizer.fit_transform(sentences)
>>>print countvectorizer.get_feature_names()
>>>print sklearn_binary.toarray()
>>>for sent_no,i in enumerate(sklearn_binary.toarray()):
>>>	results.append(sent_no,i.sum()/float(len(i.nonzero()[0])))
```


---

# Agent Instructions
This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com.

## Querying This Documentation
If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://codingling.gitbook.io/study/basic-know/yu-chu-li/todo.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.