A pure Python implementation of TextRank, based on the Mihalcea 2004 paper.
Modifications to the original Mihalcea algorithm include:
fixed bug; see Java impl, 2008
use of lemmatization instead of stemming
verbs included in the graph (but not in the resulting keyphrases)
normalized keyphrase ranks used in summarization
The results produced by this implementation are intended more for use as feature vectors in machine learning, not as academic paper summaries.
Inspired by Williams 2016 talk on text summarization.
Dependencies and Installation
This code has dependencies on several other Python projects:
To install:
pip install -r requirements.txt
The runtime depends on a local file called stop.txt which contains a list of stopwords.
Install model
After installation you need to download a language model:
python -m nltk.downloader punkt python -m nltk.downloader wordnet python -m textblob.download_corpora python -m spacy.en.download all
Example Usage
See PyTextRank wiki
Kudos
@htmartin @williamsmj @eugenep @mattkohl @HarshGrandeur @mnowotka
1 maintainer
Release files for pytextrank 1.0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| pytextrank-1.0.tar.gz | 8.4 kB | Details |
Release files / pytextrank-1.0.tar.gz
| Download URL | pytextrank-1.0.tar.gz |
|---|---|
| Size | 8.4 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
412ed08fa3982fb0fe1315bb737b753d813bf6ebcd1c81aae6d83d35e557b3f9
|
|
BLAKE2b-256 checksum How to use checksums |
94c0053428d01bd01e2047c56651c6ea67fac16945eaed8f150435390beea620
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
