TextRank Text Summarization

This is an adaptation of David Adamo's TextRank implementation using Adobe NLP Cube

Introduction

This is a python implementation of TextRank for automatic keyword and sentence extraction (summarization) as done in TextRank: Bringing Order into Texts. However, this implementation uses Levenshtein Distance as the relation between text units.

This implementation carries out automatic keyword and sentence extraction for different languages (see Adobe NLP Cube supported languages)

• Absolute or percenage based word summary
• Number of keywords extracted is relative to the size of the text (absolute or percentage based)
• Adjacent keywords in the text are concatenated into key phrases

Usage

To install the library run the setup.py module located in the repository's root directory. Alternatively, if you have access to pip you may install the library directly from github:

pip install git+git://github.com/raduenuca/text-summarization.git

Use of the library requires downloading NLP Cube resources which are downloaded once per language. You may execute the following commands against the library:

summarize --help
summarize <summary|keywords|extract-all> --help
summarize summary <filename> 
summarize keywords <filename>
summarize --language=ro extract-all "examples/data/ro/articles" "examples/data/ro"
summarize --language=en extract-all "examples/data/en/articles" "examples/data/en"

Check the language codes supported by Adobe NLP Cube

Dependencies

Dependencies are installed automatically with pip but can be installed separately.

• Networkx - https://pypi.python.org/pypi/networkx
• NLP Cube - https://pypi.python.org/pypi/nlpcube
• Click - https://pypi.python.org/pypi/click
• Numpy - https://pypi.python.org/pypi/numpy