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        "author": "Federico Barrios, Federico Lopez",
        "author_email": "summanlp@gmail.com",
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            "License :: OSI Approved :: MIT License",
            "Programming Language :: Python :: 2.7",
            "Programming Language :: Python :: 2 :: Only",
            "Topic :: Scientific/Engineering :: Information Analysis",
            "Topic :: Text Processing :: Linguistic"
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        "description": "===========================\nsumma visualization module\n===========================\n\nThis module intends to provide a visualization of the graph built by summa for\nproducing the results.\n\nUsage:\n\n    >>> text = \"\"\"Automatic summarization is the process of reducing a text document with a\n    computer program in order to create a summary that retains the most important points\n    of the original document. As the problem of information overload has grown, and as\n    the quantity of data has increased, so has interest in automatic summarization.\n    Technologies that can make a coherent summary take into account variables such as\n    length, writing style and syntax. An example of the use of summarization technology\n    is search engines such as Google. Document summarization is another.\"\"\"\n\n    >>> from visualization.export import gexf_export\n    >>> gexf_export(text, path=\"graph.gexf\")\n\nThis uses version 0.1.0 of the summarization module and works in Python 2.7.\n\n-------------\n\nSumma is open source software released under the `The MIT License (MIT) <http://opensource.org/licenses/MIT>`_.\nCopyright (c) 2014 - now Summa NLP",
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        "keywords": "summa,nlp,summarization,NLP,natural language processing,automatic summarization,keywords,summary,textrank,pagerank,visualization,export",
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