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…ght. (#72)

This was caused when calculating keywords using stopwords. As there are
more words that are filtering, the list of concepts gets smaller and the error
gets bigger.

Because of this, one of every five runs failed because the ratio between runs
was slightly different than two.

This was fixed expecting that the ratio of the runs is double with 5% tolerance
instead of up to one decimal place.
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summa – textrank

TextRank implementation for text summarization and keyword extraction in Python 3, with optimizations on the similarity function.

Features

  • Text summarization
  • Keyword extraction

Examples

Text summarization:

>>> text = """Automatic summarization is the process of reducing a text document with a \
computer program in order to create a summary that retains the most important points \
of the original document. As the problem of information overload has grown, and as \
the quantity of data has increased, so has interest in automatic summarization. \
Technologies that can make a coherent summary take into account variables such as \
length, writing style and syntax. An example of the use of summarization technology \
is search engines such as Google. Document summarization is another."""

>>> from summa import summarizer
>>> print(summarizer.summarize(text))
'Automatic summarization is the process of reducing a text document with a computer
program in order to create a summary that retains the most important points of the
original document.'

Keyword extraction:

>>> from summa import keywords
>>> print(keywords.keywords(text))
document
summarization
writing
account

Note that line breaks in the input will be used as sentence separators, so be sure to preprocess your text accordingly.

Installation

This software is available in PyPI. It depends on NumPy and Scipy, two Python libraries for scientific computing. Pip will automatically install them along with summa:

pip install summa

For a better performance of keyword extraction, install Pattern.

More examples

  • Command-line usage:

    textrank -t FILE
    
  • Define length of the summary as a proportion of the text (also available in keywords):

    >>> from summa.summarizer import summarize
    >>> summarize(text, ratio=0.2)
    
  • Define length of the summary by aproximate number of words (also available in keywords):

    >>> summarize(text, words=50)
    
  • Define input text language (also available in keywords).

    The available languages are arabic, danish, dutch, english, finnish, french, german, hungarian, italian, norwegian, polish, porter, portuguese, romanian, russian, spanish and swedish:

    >>> summarize(text, language='spanish')
    
  • Get results as a list (also available in keywords):

    >>> summarize(text, split=True)
    ['Automatic summarization is the process of reducing a text document with a
    computer program in order to create a summary that retains the most important
    points of the original document.']
    

References

To cite this work:

@article{DBLP:journals/corr/BarriosLAW16,
  author    = {Federico Barrios and
             Federico L{\'{o}}pez and
             Luis Argerich and
             Rosa Wachenchauzer},
  title     = {Variations of the Similarity Function of TextRank for Automated Summarization},
  journal   = {CoRR},
  volume    = {abs/1602.03606},
  year      = {2016},
  url       = {http://arxiv.org/abs/1602.03606},
  archivePrefix = {arXiv},
  eprint    = {1602.03606},
  timestamp = {Wed, 07 Jun 2017 14:40:43 +0200},
  biburl    = {https://dblp.org/rec/bib/journals/corr/BarriosLAW16},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

Summa is open source software released under the The MIT License (MIT).

Copyright (c) 2014 – now Summa NLP.