Python implementation of TextRank, based on the Mihalcea 2004 paper.
Modifications to the original algorithm by Rada Mihalcea, et al. include:
- fixed bug; see Java impl, 2008
- use of lemmatization instead of stemming
- verbs included in the graph (but not in the resulting keyphrases)
- named entity recognition
- 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.
See PyTextRank wiki
This code has dependencies on several other Python projects:
To install from PyPi:
pip install pytextrank
To install from this Git repo:
pip install -r requirements.txt
After installation you need to download a language model:
python -m spacy download en
Also, the runtime depends on a local file called stop.txt which
contains a list of stopwords. You can override this in the
normalize_key_phrases() call.
PyTextRank has an Apache 2.0 license, so you can use it for commercial applications. Please let us know if you find this useful, and tell us about use cases, what else you'd like to see integrated, etc.
Here's a Bibtex entry if you ever need to cite PyTextRank in a research paper:
@Misc{PyTextRank,
author = {Nathan, Paco},
title = {PyTextRank, a Python implementation of TextRank for text document NLP parsing and summarization},
howpublished = {\url{https://github.com/ceteri/pytextrank/}},
year = {2016}
}
@htmartin @williamsmj @eugenep @mattkohl @vanita5 @HarshGrandeur @mnowotka @kjam @dvsrepo