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A text summarizer

Project description

Summarizer [![Build Status](https://travis-ci.org/michigan-com/summarizer.svg)](https://travis-ci.org/michigan-com/summarizer)
==========

Summarizer is an automatic summarization algorithm.

Requirements
------------

* Python 2.7, 3.3, 3.4, or 3.5
* NLTK

Install it
----------

```
pip install summarizer
```

Use it
------

```
from summarizer import summarize
summarize(title, text)
```

Documentation
-------------

Summarizer.summarize(title, text, count=5, summarizer=Summarizer())

* title: The title of the article
* text: The actual text of the article
* count: The number of summarized sentences to return
* summarizer: The class instance that will do all the work

Contributing
------------

All contributions must be accompanied by some form of unit testing




CHANGES
=======

v0.0.7
------

* Added python 3.5 to travis
* Moved `SentenceTokenizer` to its own file
* Added `dept` to `abbrev_types`

v0.0.6
------

* [FIX] Sgt, Gov, and No abbreviations accounted for

v0.0.5 10-01-2015
-----------------

* [FIX] Python2.7 unicode errors
* Updated PunktLanguageVars to detect special quotation marks

v0.0.4 10-01-2015
-----------------

* [FIX] PYPI didn't pick up the new training data in JSON format

v0.0.3 10-01-2015
-----------------

* [FIX] Tokenizer would think a custom abbreviation, F.B.I., was a sentence break
* [FIX] Added sanitizer module to preprocess text before summarizing it
* [TESTS] Added ability to pull valid tokenized articles from brevity.detroitnow.io
and test that the new tokenizer is still valid

v0.0.2 08-26-2015
-----------------

* PYPI not picking up data files

v0.0.1 08-26-2015
-----------------

* Added setup.py
* Added \_\_version\_\_
* Added unit tests

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