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\u003chttps://arxiv.org/pdf/1602.03606.pdf\u003e`_.\n\n\nFeatures\n--------\n\n* Text summarization\n* Keyword extraction\n\nExamples\n--------\n\nText summarization::\n\n    \u003e\u003e\u003e 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    \u003e\u003e\u003e from summa import summarizer\n    \u003e\u003e\u003e print(summarizer.summarize(text))\n    'Automatic summarization is the process of reducing a text document with a computer\n    program in order to create a summary that retains the most important points of the\n    original document.'\n\n\nKeyword extraction::\n\n    \u003e\u003e\u003e from summa import keywords\n    \u003e\u003e\u003e print(keywords.keywords(text))\n    document\n    summarization\n    writing\n    account\n\n\nNote that line breaks in the input will be used as sentence separators, so be sure\nto preprocess your text accordingly.\n\nInstallation\n------------\n\nThis software is `available in PyPI \u003chttps://pypi.org/project/summa/\u003e`_.\nIt depends on `NumPy \u003chttp://www.numpy.org/\u003e`_ and `Scipy \u003chttps://www.scipy.org/\u003e`_,\ntwo Python libraries for scientific computing.\nPip will automatically install them along with `summa`::\n\n    pip install summa\n\nFor a better performance of keyword extraction, install `Pattern \u003chttp://www.clips.ua.ac.be/pattern\u003e`_.\n\n\nMore examples\n-------------\n\n- Command-line usage::\n\n    textrank -t FILE\n\n- Define length of the summary as a proportion of the text (also available in :code:`keywords`)::\n\n    \u003e\u003e\u003e from summa.summarizer import summarize\n    \u003e\u003e\u003e summarize(text, ratio=0.2)\n\n- Define length of the summary by aproximate number of words (also available in :code:`keywords`)::\n\n    \u003e\u003e\u003e summarize(text, words=50)\n\n- Define input text language (also available in :code:`keywords`).\n\n  The available languages are arabic, danish, dutch, english, finnish, french, german,\n  hungarian, italian, norwegian, polish, porter, portuguese, romanian, russian,\n  spanish and swedish::\n\n\n    \u003e\u003e\u003e summarize(text, language='spanish')\n\n- Get results as a list (also available in :code:`keywords`)::\n\n    \u003e\u003e\u003e summarize(text, split=True)\n    ['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\n    points of the original document.']\n\n\nReferences\n-------------\n- Mihalcea, R., Tarau, P.:\n  `\"Textrank: Bringing order into texts\" \u003chttp://www.aclweb.org/anthology/W04-3252\u003e`__.\n  In: Lin, D., Wu, D. (eds.)\n  Proceedings of EMNLP 2004. pp. 404–411. Association for Computational Linguistics,\n  Barcelona, Spain. July 2004.\n\n- Barrios, F., López, F., Argerich, L., Wachenchauzer, R.:\n  `\"Variations of the Similarity Function of TextRank for Automated Summarization\" \u003chttps://arxiv.org/pdf/1602.03606.pdf\u003e`__.\n  Anales de las 44JAIIO.\n  Jornadas Argentinas de Informática, Argentine Symposium on Artificial Intelligence, 2015.\n\n\nTo cite this work::\n\n    @article{DBLP:journals/corr/BarriosLAW16,\n      author    = {Federico Barrios and\n                 Federico L{\\'{o}}pez and\n                 Luis Argerich and\n                 Rosa Wachenchauzer},\n      title     = {Variations of the Similarity Function of TextRank for Automated Summarization},\n      journal   = {CoRR},\n      volume    = {abs/1602.03606},\n      year      = {2016},\n      url       = {http://arxiv.org/abs/1602.03606},\n      archivePrefix = {arXiv},\n      eprint    = {1602.03606},\n      timestamp = {Wed, 07 Jun 2017 14:40:43 +0200},\n      biburl    = {https://dblp.org/rec/bib/journals/corr/BarriosLAW16},\n      bibsource = {dblp computer science bibliography, https://dblp.org}\n    }\n\n\n-------------\n\nSumma is open source software released under the `The MIT License (MIT) \u003chttp://opensource.org/licenses/MIT\u003e`_.\n\nCopyright (c) 2014 – now Summa NLP.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsummanlp%2Ftextrank","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fsummanlp%2Ftextrank","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsummanlp%2Ftextrank/lists"}