README

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summa – textrank
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TextRank implementation for text summarization and keyword extraction in Python 3,
with `optimizations on the similarity function <https://arxiv.org/pdf/1602.03606.pdf>`_.


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


Installation
------------

This software depends on `NumPy <http://www.numpy.org/>`_ and `Scipy <https://www.scipy.org/>`_,
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 <http://www.clips.ua.ac.be/pattern>`_.


More examples
-------------

- Command-line usage::

    textrank -t FILE

- Define length of the summary as a proportion of the text (also available in :code:`keywords`)::

    >>> from summa.summarizer import summarize
    >>> summarize(text, ratio=0.2)

- Define length of the summary by aproximate number of words (also available in :code:`keywords`)::

    >>> summarize(text, words=50)

- Define input text language (also available in :code:`keywords`):

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

  >>> summarize(text, language='spanish')

- Get results as a list (also available in :code:`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
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- Mihalcea, R., Tarau, P.:
  `"Textrank: Bringing order into texts" <http://www.aclweb.org/anthology/W04-3252>`__.
  In: Lin, D., Wu, D. (eds.)
  Proceedings of EMNLP 2004. pp. 404–411. Association for Computational Linguistics,
  Barcelona, Spain. July 2004.

- Barrios, F., López, F., Argerich, L., Wachenchauzer, R.:
  `"Variations of the Similarity Function of TextRank for Automated Summarization" <https://arxiv.org/pdf/1602.03606.pdf>`__.
  Anales de las 44JAIIO.
  Jornadas Argentinas de Informática, Argentine Symposium on Artificial Intelligence, 2015.


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}
    }


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Summa is open source software released under the `The MIT License (MIT) <http://opensource.org/licenses/MIT>`_.

Copyright (c) 2014 – now Summa NLP.