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https://github.com/davidadamojr/TextRank
Python implementation of TextRank algorithm for automatic keyword extraction and summarization using Levenshtein distance as relation between text units. This project is based on the paper "TextRank: Bringing Order into Text" by Rada Mihalcea and Paul Tarau. https://web.eecs.umich.edu/~mihalcea/papers/mihalcea.emnlp04.pdf
https://github.com/davidadamojr/TextRank
Last synced: 3 months ago
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Python implementation of TextRank algorithm for automatic keyword extraction and summarization using Levenshtein distance as relation between text units. This project is based on the paper "TextRank: Bringing Order into Text" by Rada Mihalcea and Paul Tarau. https://web.eecs.umich.edu/~mihalcea/papers/mihalcea.emnlp04.pdf
- Host: GitHub
- URL: https://github.com/davidadamojr/TextRank
- Owner: davidadamojr
- Created: 2013-11-24T16:56:40.000Z (about 11 years ago)
- Default Branch: master
- Last Pushed: 2022-05-05T15:12:55.000Z (almost 3 years ago)
- Last Synced: 2024-08-02T16:30:32.366Z (6 months ago)
- Language: Python
- Homepage:
- Size: 40 KB
- Stars: 758
- Watchers: 40
- Forks: 224
- Open Issues: 7
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
- awesome-pdf - TextRank
README
## TextRank
This is a python implementation of TextRank for automatic keyword and sentence extraction (summarization) as done in https://web.eecs.umich.edu/~mihalcea/papers/mihalcea.emnlp04.pdf. However, this implementation uses Levenshtein Distance as the relation between text units.
This implementation carries out automatic keyword and sentence extraction on 10 articles gotten from http://theonion.com
- 100 word summary
- Number of keywords extracted is relative to the size of the text (a third of the number of nodes in the graph)
- Adjacent keywords in the text are concatenated into keyphrases
### 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+https://github.com/davidadamojr/TextRank.git
```
Use of the library requires downloading nltk resources. Use the `textrank initialize` command to fetch the required data. Once the data has finished downloading you may execute the following commands against the library:
```
textrank extract_summary
textrank extract_phrases
```
### Contributing
Install the library as "editable" within a virtual environment.
```
pip install -e .
```
### Dependencies
Dependencies are installed automatically with pip but can be installed serparately.
* Networkx - https://pypi.python.org/pypi/networkx/
* NLTK 3.0 - https://pypi.python.org/pypi/nltk/3.2.2
* Numpy - https://pypi.python.org/pypi/numpy
* Click - https://pypi.python.org/pypi/click