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    "info": {
        "author": "Remi Bois",
        "author_email": "remibois.contact@gmail.com",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 5 - Production/Stable",
            "License :: OSI Approved :: MIT License",
            "Programming Language :: Python :: 2",
            "Programming Language :: Python :: 2.7",
            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3.4",
            "Programming Language :: Python :: 3.5",
            "Programming Language :: Python :: 3.6",
            "Topic :: Scientific/Engineering :: Information Analysis",
            "Topic :: Text Processing :: Linguistic"
        ],
        "description": "[![Build Status](https://travis-ci.org/sildar/potara.svg?branch=master)](https://travis-ci.org/sildar/potara)\n[![Coverage Status](https://coveralls.io/repos/sildar/potara/badge.png?branch=master)](https://coveralls.io/r/sildar/potara?branch=master)\n[![Requirements Status](https://requires.io/github/sildar/potara/requirements.svg?branch=master)](https://requires.io/github/sildar/potara/requirements/?branch=master)\n\n# Basics\n\nPotara is a **multi-document** summarization system that relies on Integer\nLinear Programming (ILP) and sentence fusion.\n\nIts goal is to summarize a set of related documents in a few sentences.\nIt proceeds by fusing similar sentences in order to create sentences\nthat are either shorter or more informative than those found in the\ndocuments.\nIt then uses ILP in order to choose the best set of sentences, fused\nor not, that will compose the resulting summary.\n\nIt relies on state-of-the-art (as of 2014) approaches introduced by Gillick and\nFavre for the ILP strategy, and Filippova for the sentence fusion.\n\n# Install\n\n## The easy way\n\nYou should be able to install potara and its dependencies with pip\n\n```\npip install potara\n```\n\nYou can also clone this repo and use the requirements.txt file to install dependencies\n\n## further requirements\n\nYou will also need GLPK, which is used to obtain an optimal summary\n(example for Debian-based distro)\n\n```\n$ sudo apt-get install glpk\n```\n\nFor Ubuntu-based distros you can use:\n```\n$ sudo apt-get install libglpk40\n```\n\nYou can check that the install run successfully by cloning the repo and running\n\n```\n$ python setup.py test\n```\n\nIf you have issues with install, you can check the .travis.yml file of the repo, which corresponds to a working build.\n\n# How To\n\nBasically, you can use the following\n\n```\nfrom summarizer import Summarizer\nimport document\n\ns = Summarizer()\n\n# Adding docs, preprocessing them and computing some infos for the summarizer\ns.setDocuments([document.Document('data/' + str(i) + '.txt')\n                for i in range(1,10)])\n\n# Summarizing, where the actual work is done\ns.summarize()\n\n# You can then print the summary\nprint(s.summary)\n```\n\nThere's some preprocessing involved and a sentence fusion step, but I\nmade it easily tunable. Preprocessing may take a while (a few minutes)\nsince there is a lot going on under the hood. Default parameters are \ncurrently set for summarizing ~10 documents. You can summarize a smaller\namount of documents by tweaking the \"minbigramcount\" parameter of the\nsummarizer :\n\n`s = Summarizer(minbigramcount=2)`\n\nSummarizing less than 4 documents would probably yield a bad\nsummary.\n\n\n",
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        "home_page": "https://github.com/sildar/potara",
        "keywords": "summarization",
        "license": "Apache",
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        "name": "potara",
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        "summary": "A multi-document summarizer based on ILP and sentence fusion.",
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