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        "author": "Paco Nathan",
        "author_email": "ceteri@gmail.com",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 5 - Production/Stable",
            "Intended Audience :: Developers",
            "Intended Audience :: Education",
            "Intended Audience :: Information Technology",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: Apache Software License",
            "Operating System :: OS Independent",
            "Programming Language :: Python :: 3",
            "Topic :: Scientific/Engineering :: Artificial Intelligence",
            "Topic :: Scientific/Engineering :: Human Machine Interfaces",
            "Topic :: Scientific/Engineering :: Information Analysis",
            "Topic :: Text Processing :: General",
            "Topic :: Text Processing :: Indexing",
            "Topic :: Text Processing :: Linguistic"
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        "description": "Python impl for TextRank\n========================\n\nPython implementation of *TextRank*, based on the \n`Mihalcea 2004 <http://web.eecs.umich.edu/~mihalcea/papers/mihalcea.emnlp04.pdf>`_\npaper.\n\nModifications to the original algorithm by\n`Rada Mihalcea <https://web.eecs.umich.edu/~mihalcea/>`_, et al.\ninclude:\n\n-  fixed bug; see `Java impl, 2008 <https://github.com/ceteri/textrank>`_\n-  use of lemmatization instead of stemming\n-  verbs included in the graph (but not in the resulting keyphrases)\n-  named entity recognition\n-  normalized keyphrase ranks used in summarization\n\nThe results produced by this implementation are intended more for use\nas *feature vectors* in machine learning, not as academic paper\nsummaries.\n\nInspired by `Williams 2016 <http://mike.place/2016/summarization/>`_\ntalk on *text summarization*.\n\n\nExample Usage\n-------------\n\nSee `PyTextRank wiki <https://github.com/ceteri/pytextrank/wiki/Examples>`_\n\n\nDependencies and Installation\n-----------------------------\n\nThis code has dependencies on several other Python projects:\n\n-  `spaCy <https://spacy.io/docs/usage/>`_\n-  `NetworkX <http://networkx.readthedocs.io/>`_\n-  `datasketch <https://github.com/ekzhu/datasketch>`_\n-  `graphviz <https://pypi.python.org/pypi/graphviz>`_\n\nTo install from `PyPi <https://pypi.python.org/pypi/pytextrank>`_:\n\n::\n\n    pip install pytextrank\n\n\nTo install from this Git repo:\n\n::\n\n    pip install -r requirements.txt\n\nAfter installation you need to download a language model:\n\n::\n\n    python -m spacy download en\n\nAlso, the runtime depends on a local file called ``stop.txt`` which\ncontains a list of *stopwords*. You can override this in the\n`normalize_key_phrases()` call.\n\n\nKudos\n-----\n\n`@htmartin <https://github.com/htmartin>`_\n`@williamsmj <https://github.com/williamsmj/>`_\n`@eugenep <https://github.com/eugenep/>`_\n`@mattkohl <https://github.com/mattkohl>`_\n`@vanita5 <https://github.com/vanita5>`_\n`@HarshGrandeur <https://github.com/HarshGrandeur>`_\n`@mnowotka <https://github.com/mnowotka>`_\n`@kjam <https://github.com/kjam>`_",
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        "keywords": "textrank,parsing,automatic summarization,natural language processing,nlp,text analytics",
        "license": "Apache License 2.0",
        "maintainer": "",
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        "name": "pytextrank",
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