A pure Python impl of TextRank for document summarization
Python Shell


Python impl for TextRank

A pure Python implementation of TextRank, based on the Mihalcea 2004 paper. This work leads toward integration with the Williams 2016 talk on text summarization.

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

Dependencies and Installation

This code has dependencies on several other Python projects:

To install:

pip install textblob
pip install -U git+https://github.com/sloria/textblob-aptagger.git@dev
python -m nltk.downloader punkt
python -m nltk.downloader wordnet
python -m textblob.download_corpora
pip install -U spacy
python -m spacy.en.download all
pip install networkx
pip install statistics
pip install datasketch -U
pip install graphviz

NB: the runtime depends on a local file called stop.txt which contains a list of stopwords.

Example Usage

Run a test case based on the Mihalcea paper:

./stage1.py dat/mih.json > out1.json
./stage2.py out1.json > out2.json
0.1286  types systems
0.0922  mixed types
0.0711  minimal set
0.0643  systems
0.0546  strict inequations
0.0474  considered
0.0461  types
0.0368  natural numbers
0.0355  minimal supporting set
0.0355  set
0.0351  solutions
0.0321  linear diophantine equations
0.0291  linear constraints
0.0286  solving
0.0275  corresponding algorithms

NB: results for this implementation are intended more to be used as feature vectors, not as academic paper summaries.

Run another test based on Williams, using text from a Wired article:

./stage1.py dat/lee.json > out1.json
./stage2.py out1.json > out2.json
./stage3.py out1.json out2.json > out3.json
./stage4.py out2.json out3.json > out4.md

Which produces as a summary:

excerpts: The surprisingly skillful Google machine, known as AlphaGo, now needs only one more win to claim victory in the match. The Korean-born Lee Sedol will go down in defeat unless he takes each of the match's last three games. Game Three is set for Saturday afternoon inside Seoul's Four Seasons hotel. Lee Sedol is widely-regarded as the top Go player of the last decade, after winning more international titles than all but one other player. Although AlphaGo topped Lee Sedol in the match's first game on Wednesday afternoon, the outcome of Game Two was no easier to predict. In his 1996 match with IBM's Deep Blue supercomputer, world chess champion Gary Kasparov lost the first game but then came back to win the second game and, eventually, the match as a whole.

keywords: first game, google ai lab, all-important match, lee sedol, more win, alphago, wednesday, seoul, seasons hotel, world chess champion gary kasparov, afternoon, game, second game

These results show a summarization similar to slide 30 of the talk; however, this approach is more amenable to:

  • bootstrapping work with new documents about a specific topic
  • producing results ready for use in a search engine or recommender system

NB: Unicode

Note the force_encode flags on some of the function calls. This forces utf-8 encoding, in case the input has characters that couldn't be handled otherwise. That may require some post-processing for your use cases -- see examples functions in the stage4.py code. This is turned off by default.

TODO: Stay tuned for more...

  1. Docker container for managing the installation/dependencies


@htmartin @williamsmj @mattkohl @HarshGrandeur @mnowotka