Metadata-Version: 1.2
Name: summa
Version: 1.1.0
Summary: A text summarization and keyword extraction package based on TextRank
Home-page: https://github.com/summanlp/textrank
Author: Federico Barrios, Federico Lopez
Author-email: UNKNOWN
License: UNKNOWN
Download-URL: https://github.com/summanlp/textrank/releases
Description: ================
        summa – textrank
        ================
        
        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
        -------------
        - 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}
            }
        
        
        -------------
        
        Summa is open source software released under the `The MIT License (MIT) <http://opensource.org/licenses/MIT>`_.
        
        Copyright (c) 2014 – now Summa NLP.
        
Keywords: summa,nlp,summarization,NLP,natural language processing,automatic summarization,keywords,summary,textrank,pagerank
Platform: UNKNOWN
Classifier: Development Status :: 5 - Production/Stable
Classifier: License :: OSI Approved :: MIT License
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Education
Classifier: Topic :: Scientific/Engineering :: Information Analysis
Classifier: Topic :: Text Processing :: Linguistic
Classifier: Programming Language :: Python :: 3 :: Only
Requires-Python: >=3.4
