Metadata-Version: 1.2
Name: summa
Version: 1.2.0
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
        
        
        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 <https://pypi.org/project/summa/>`_.
        It 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 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 :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
