Metadata-Version: 2.1
Name: pytextrank
Version: 3.1.1
Summary: Python implementation of TextRank as a spaCy pipeline extension, for graph-based natural language work plus related knowledge graph practices; used for for phrase extraction and lightweight extractive summarization of text documents.
Home-page: https://derwen.ai/docs/ptr/
Author: Paco Nathan
Author-email: paco@derwen.ai
License: MIT
Project-URL: Source, http://github.com/DerwenAI/pytextrank
Project-URL: spaCy uniVerse, https://spacy.io/universe/project/spacy-pytextrank
Project-URL: Issue Tracker, https://github.com/DerwenAI/pytextrank/issues
Project-URL: Discussion Forum, https://www.linkedin.com/groups/6725785/
Project-URL: StackOverflow, https://stackoverflow.com/search?q=pytextrank
Project-URL: Citations, https://scholar.google.com/scholar?q=related:5tl6J4xZlCIJ:scholar.google.com/&scioq=&hl=en&as_sdt=0,5
Description: # PyTextRank
        
        [![DOI](https://zenodo.org/badge/69814684.svg)](https://zenodo.org/badge/latestdoi/69814684)
        ![Licence](https://img.shields.io/github/license/DerwenAI/pytextrank)
        ![Repo size](https://img.shields.io/github/repo-size/DerwenAI/pytextrank)
        ![GitHub commit activity](https://img.shields.io/github/commit-activity/w/DerwenAI/pytextrank?style=plastic)
        [![Checked with mypy](http://www.mypy-lang.org/static/mypy_badge.svg)](http://mypy-lang.org/)
        [![security: bandit](https://img.shields.io/badge/security-bandit-yellow.svg)](https://github.com/PyCQA/bandit)
        
        **PyTextRank** is a Python implementation of *TextRank* as a
        [spaCy pipeline extension](https://spacy.io/universe/project/spacy-pytextrank),
        for graph-based natural language work -- and related knowledge graph practices.
        This includes the [*textgraphs*](http://www.textgraphs.org/) algorithms:
        
          - *TextRank* by [[mihalcea04textrank]](https://derwen.ai/docs/ptr/biblio/#mihalcea04textrank)
          - *PositionRank* by [[florescuc17]](https://derwen.ai/docs/ptr/biblio/#florescuc17)
          - *Biased TextRank* by [[kazemi2011corr]](https://derwen.ai/docs/ptr/biblio/#kazemi2011corr)
        
        Popular use cases for this library include:
        
          - *phrase extraction*: get the top-ranked phrases from a text document
          - low-cost *extractive summarization* of a text document
          - help infer links from unstructured text into more structured representation
        
        See our full documentation at: <https://derwen.ai/docs/ptr/>
        
        
        ## Getting Started
        
        See the ["Getting Started"](https://derwen.ai/docs/ptr/start/)
        section of the online documentation.
        
        To install from [PyPi](https://pypi.python.org/pypi/pytextrank):
        ```
        python3 -m pip install pytextrank
        python3 -m spacy download en_core_web_sm
        ```
        
        If you work directly from this Git repo, be sure to install the
        dependencies as well:
        ```
        python3 -m pip install -r requirements.txt
        ```
        
        Alternatively, to install dependencies using `conda`:
        ```
        conda env create -f environment.yml
        conda activate pytextrank
        ```
        
        Then to use the library with a simple use case:
        ```python
        import spacy
        import pytextrank
        
        # example text
        text = "Compatibility of systems of linear constraints over the set of natural numbers. Criteria of compatibility of a system of linear Diophantine equations, strict inequations, and nonstrict inequations are considered. Upper bounds for components of a minimal set of solutions and algorithms of construction of minimal generating sets of solutions for all types of systems are given. These criteria and the corresponding algorithms for constructing a minimal supporting set of solutions can be used in solving all the considered types systems and systems of mixed types."
        
        # load a spaCy model, depending on language, scale, etc.
        nlp = spacy.load("en_core_web_sm")
        
        # add PyTextRank to the spaCy pipeline
        nlp.add_pipe("textrank")
        doc = nlp(text)
        
        # examine the top-ranked phrases in the document
        for phrase in doc._.phrases:
            print(phrase.text)
            print(phrase.rank, phrase.count)
            print(phrase.chunks)
        ```
        
        See the **tutorial notebooks** in the `examples` subdirectory for
        sample code and patterns to use in integrating **PyTextTank** with
        related libraries in Python:
        <https://derwen.ai/docs/ptr/tutorial/>
        
        
        <details>
          <summary>Contributing Code</summary>
        
        We welcome people getting involved as contributors to this open source
        project!
        
        For detailed instructions please see:
        [CONTRIBUTING.md](https://github.com/DerwenAI/pytextrank/blob/main/CONTRIBUTING.md)
        </details>
        
        <details>
          <summary>Build Instructions</summary>
        
        <strong>
        Note: unless you are contributing code and updates,
        in most use cases won't need to build this package locally.
        </strong>
        
        Instead, simply install from
        [PyPi](https://pypi.python.org/pypi/pytextrank)
        or use [Conda](https://docs.conda.io/).
        
        To set up the build environment locally, see the 
        ["Build Instructions"](https://derwen.ai/docs/ptr/build/)
        section of the online documentation.
        </details>
        
        <details>
          <summary>Semantic Versioning</summary>
        
        Generally speaking the major release number of <strong>PyTextRank</strong> 
        will track with the major release number of the associated <code>spaCy</code>
        version.
        
        See:
        [changelog.txt](https://github.com/DerwenAI/pytextrank/blob/main/changelog.txt)
        </details>
        
        <img
         alt="thanks noam!"
         src="https://raw.githubusercontent.com/DerwenAI/pytextrank/main/docs/assets/noam.jpg"
         width="231"
        />
        
        
        ## License and Copyright
        
        Source code for **PyTextRank** plus its logo, documentation, and examples
        have an [MIT license](https://spdx.org/licenses/MIT.html) which is
        succinct and simplifies use in commercial applications.
        
        All materials herein are Copyright &copy; 2016-2021 Derwen, Inc.
        
        
        ## Attribution
        
        Please use the following BibTeX entry for citing **PyTextRank** if you 
        use it in your research or software:
        ```bibtex
        @software{PyTextRank,
          author = {Paco Nathan},
          title = {{PyTextRank, a Python implementation of TextRank for phrase extraction and summarization of text documents}},
          year = 2016,
          publisher = {Derwen},
          doi = {10.5281/zenodo.4602393},
          url = {https://github.com/DerwenAI/pytextrank}
        }
        ```
        
        Citations are helpful for the continued development and maintenance of
        this library.
        For example, see our citations listed on
        [Google Scholar](https://scholar.google.com/scholar?q=related:5tl6J4xZlCIJ:scholar.google.com/&scioq=&hl=en&as_sdt=0,5).
        
        
        ## Kudos
        
        Many thanks to our contributors:
        [@louisguitton](https://github.com/louisguitton),
        [@Ankush-Chander](https://github.com/Ankush-Chander),
        [@Lord-V15](https://github.com/Lord-V15),
        [@anna-droid-beep](https://github.com/anna-droid-beep),
        [@dvsrepo](https://github.com/dvsrepo),
        [@kavorite](https://github.com/kavorite),
        [@htmartin](https://github.com/htmartin),
        [@williamsmj](https://github.com/williamsmj/),
        [@mattkohl](https://github.com/mattkohl),
        [@vanita5](https://github.com/vanita5),
        [@HarshGrandeur](https://github.com/HarshGrandeur),
        [@mnowotka](https://github.com/mnowotka),
        [@kjam](https://github.com/kjam),
        [@SaiThejeshwar](https://github.com/SaiThejeshwar),
        [@laxatives](https://github.com/laxatives),
        [@dimmu](https://github.com/dimmu), 
        [@JasonZhangzy1757](https://github.com/JasonZhangzy1757), 
        [@jake-aft](https://github.com/jake-aft),
        [@junchen1992](https://github.com/junchen1992),
        [@shyamcody](https://github.com/shyamcody),
        [@chikubee](https://github.com/chikubee),
        outstanding NLP research work led by [@mihalcea](https://github.com/mihalcea),
        encouragement from the wonderful folks at Explosion who develop [spaCy](https://github.com/explosion/spaCy),
        plus general support from [Derwen, Inc.](https://derwen.ai/)
        
Keywords: biased textrank,entity linking,extractive summarization,graph algorithms,knowledge graph,natural language processing,nlp,parsing,phrase extraction,pipeline component,positionrank,spacy,text analytics,textgraphs,textrank
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Education
Classifier: Intended Audience :: Information Technology
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Scientific/Engineering :: Human Machine Interfaces
Classifier: Topic :: Scientific/Engineering :: Information Analysis
Classifier: Topic :: Text Processing :: General
Classifier: Topic :: Text Processing :: Indexing
Classifier: Topic :: Text Processing :: Linguistic
Requires-Python: >=3.7
Description-Content-Type: text/markdown
Provides-Extra: base
Provides-Extra: docs
