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    "info": {
        "author": "Mi\u0161o Belica",
        "author_email": "miso.belica@gmail.com",
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        "classifiers": [
            "Development Status :: 4 - Beta",
            "Intended Audience :: Developers",
            "Intended Audience :: Education",
            "License :: OSI Approved :: Apache Software License",
            "Natural Language :: Chinese (Simplified)",
            "Natural Language :: Czech",
            "Natural Language :: English",
            "Natural Language :: French",
            "Natural Language :: German",
            "Natural Language :: Italian",
            "Natural Language :: Japanese",
            "Natural Language :: Portuguese",
            "Natural Language :: Slovak",
            "Natural Language :: Spanish",
            "Operating System :: OS Independent",
            "Programming Language :: Python",
            "Programming Language :: Python :: 2",
            "Programming Language :: Python :: 2.7",
            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3.3",
            "Programming Language :: Python :: 3.4",
            "Programming Language :: Python :: 3.5",
            "Programming Language :: Python :: 3.6",
            "Programming Language :: Python :: 3.7",
            "Programming Language :: Python :: Implementation :: CPython",
            "Topic :: Education",
            "Topic :: Internet",
            "Topic :: Scientific/Engineering :: Information Analysis",
            "Topic :: Text Processing :: Filters",
            "Topic :: Text Processing :: Linguistic",
            "Topic :: Text Processing :: Markup :: HTML"
        ],
        "description": "# Automatic text summarizer\n\n[![image](https://api.travis-ci.org/miso-belica/sumy.png?branch=master)](https://travis-ci.org/miso-belica/sumy)\n\nSimple library and command line utility for extracting summary from HTML\npages or plain texts. The package also contains simple evaluation\nframework for text summaries. Implemented summarization methods:\n\n-   **Luhn** - heurestic method,\n    [reference](http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=5392672)\n-   **Edmundson** heurestic method with previous statistic research,\n    [reference](http://dl.acm.org/citation.cfm?doid=321510.321519)\n-   **Latent Semantic Analysis, LSA** - one of the algorithm from\n    <http://scholar.google.com/citations?user=0fTuW_YAAAAJ&hl=en> I\n    think the author is using more advanced algorithms now.\n    [Steinberger, J. a Je\u0139\u013eek, K. Using latent semantic an and\n    summary evaluation. In In Proceedings ISIM '04. 2004. S.\n    93-100.](http://www.kiv.zcu.cz/~jstein/publikace/isim2004.pdf)\n-   **LexRank** - Unsupervised approach inspired by algorithms PageRank\n    and HITS,\n    [reference](http://tangra.si.umich.edu/~radev/lexrank/lexrank.pdf)\n-   **TextRank** - Unsupervised approach, also using PageRank algorithm,\n    [reference](https://web.eecs.umich.edu/~mihalcea/papers/mihalcea.emnlp04.pdf)\n-   **SumBasic** - Method that is often used as a baseline in\n    the literature. Source: [Read about\n    SumBasic](http://www.cis.upenn.edu/~nenkova/papers/ipm.pdf)\n-   **KL-Sum** - Method that greedily adds sentences to a summary so\n    long as it decreases the KL Divergence. Source: [Read about\n    KL-Sum](http://www.aclweb.org/anthology/N09-1041)\n-   **Reduction** - Graph-based summarization, where a sentence salience is\n    computed as the sum of the weights of its edges to other sentences. The\n    weight of an edge between two sentences is computed in the same manner\n    as TextRank.\n\nHere are some other summarizers:\n\n-   <https://github.com/thavelick/summarize/> - Python, TF (very simple)\n-   [Reduction](https://github.com/adamfabish/Reduction) - Python,\n    TextRank (simple)\n-   [Open Text Summarizer](http://libots.sourceforge.net/) - C, TF\n    without normalization\n-   [Simple program that summarize\n    text](https://github.com/xhresko/text-summarizer) - Python, TF\n    without normalization\n-   [Intro to Computational\n    Linguistics](https://github.com/kylehardgrave/summarizer) - Java,\n    LexRank\n-   [Sumtract: Second project for UW LING\n    572](https://github.com/stefanbehr/sumtract) - Python\n-   [TextTeaser](https://github.com/MojoJolo/textteaser) - Scala\n-   [PyTeaser](https://github.com/xiaoxu193/PyTeaser) - TextTeaser port\n    in Python\n-   [Automatic Document\n    Summarizer](https://github.com/himanshujindal/Automatic-Text-Summarizer) -\n    Java, Bipartite HITS (no sources)\n-   [Pythia](https://github.com/giorgosera/pythia/blob/dev/analysis/summarization/summarization.py) -\n    Python, LexRank & Centroid\n-   [SWING](https://github.com/WING-NUS/SWING) - Ruby\n-   [Topic Networks](https://github.com/bobflagg/Topic-Networks) - R,\n    topic models & bipartite graphs\n-   [Almus: Automatic Text\n    Summarizer](http://textmining.zcu.cz/?lang=en&section=download) -\n    Java, LSA (without source code)\n-   [Musutelsa](http://www.musutelsa.jamstudio.eu/) - Java, LSA\n    (always freezes)\n-   <http://mff.bajecni.cz/index.php> - C++\n-   [MEAD](http://www.summarization.com/mead/) - Perl, various methods +\n    evaluation framework\n\n## Installation\n\nMake sure you have [Python](http://www.python.org/) 2.7/3.3+ and\n[pip](https://crate.io/packages/pip/)\n([Windows](http://docs.python-guide.org/en/latest/starting/install/win/),\n[Linux](http://docs.python-guide.org/en/latest/starting/install/linux/))\ninstalled. Run simply (preferred way):\n\n```sh\n$ [sudo] pip install sumy\n```\n\nOr for the fresh version:\n\n```sh\n$ [sudo] pip install git+git://github.com/miso-belica/sumy.git\n```\n\n## Usage\n\nSumy contains command line utility for quick summarization of documents.\n\n```sh\n$ sumy lex-rank --length=10 --url=http://en.wikipedia.org/wiki/Automatic_summarization # what's summarization?\n$ sumy luhn --language=czech --url=http://www.zdrojak.cz/clanky/automaticke-zabezpeceni/\n$ sumy edmundson --language=czech --length=3% --url=http://cs.wikipedia.org/wiki/Bitva_u_Lipan\n$ sumy --help # for more info\n```\n\nVarious evaluation methods for some summarization method can be executed\nby commands below:\n\n```sh\n$ sumy_eval lex-rank reference_summary.txt --url=http://en.wikipedia.org/wiki/Automatic_summarization\n$ sumy_eval lsa reference_summary.txt --language=czech --url=http://www.zdrojak.cz/clanky/automaticke-zabezpeceni/\n$ sumy_eval edmundson reference_summary.txt --language=czech --url=http://cs.wikipedia.org/wiki/Bitva_u_Lipan\n$ sumy_eval --help # for more info\n```\n\n## Python API\n\nOr you can use sumy like a library in your project. Create file `sumy_example.py` ([don't name it `sumy.py`](https://stackoverflow.com/questions/41334622/python-sumy-no-module-named-sumy-parsers-html)) with the code below to test it.\n\n```python\n# -*- coding: utf-8 -*-\n\nfrom __future__ import absolute_import\nfrom __future__ import division, print_function, unicode_literals\n\nfrom sumy.parsers.html import HtmlParser\nfrom sumy.parsers.plaintext import PlaintextParser\nfrom sumy.nlp.tokenizers import Tokenizer\nfrom sumy.summarizers.lsa import LsaSummarizer as Summarizer\nfrom sumy.nlp.stemmers import Stemmer\nfrom sumy.utils import get_stop_words\n\n\nLANGUAGE = \"english\"\nSENTENCES_COUNT = 10\n\n\nif __name__ == \"__main__\":\n    url = \"https://en.wikipedia.org/wiki/Automatic_summarization\"\n    parser = HtmlParser.from_url(url, Tokenizer(LANGUAGE))\n    # or for plain text files\n    # parser = PlaintextParser.from_file(\"document.txt\", Tokenizer(LANGUAGE))\n    stemmer = Stemmer(LANGUAGE)\n\n    summarizer = Summarizer(stemmer)\n    summarizer.stop_words = get_stop_words(LANGUAGE)\n\n    for sentence in summarizer(parser.document, SENTENCES_COUNT):\n        print(sentence)\n```\n\n\n",
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