summpy 0.2.1
Text summarization (sentence extraction) module with simple HTTP API. (currently, supports Japanese only)
License: MIT
Keywords: automatic summarization,natural language processing

Summpy
======

Text summarization (sentence extraction) module with simple HTTP API.
(Currently supports Japanese only)

License
-------

MIT License

Requirements
------------

Python 2.7.\*
~~~~~~~~~~~~~

-  numpy
-  scipy
-  scikit-learn
-  networkx
-  cherrypy
-  MeCab or janome
-  pulp (if you use ILP-based method)

Quick start
-----------

.. code:: sh

    pip install summpy
    python -m summpy.server -h 127.0.0.1 -p 8080
    curl http://127.0.0.1:8080/summarize\?sent_limit\=3\&text\=要約したい文章を入力。

Input Parameters
~~~~~~~~~~~~~~~~

-  ``text``: text (utf-8)
-  ``algo``: (optional)

   -  ``lexrank``: LexRank, a graph-based summarization (default)
   -  ``clexrank``: Continuous LexRank
   -  ``divrank``: (experimental) DivRank (Diverse Rank, graph-based
      method). Since DivRank aims to provide non-redundant and high
      coverage information, it is suitable for multi-document
      summarization.
   -  ``mcp``: ILP-based method. Extracts sentences in terms of Maximum
      Coverage Problem.

Hyper parameters for how many sentences are shown (optional)

-  ``sent_limit``: number of sentences (only {lex,clex,div}rank)
-  ``char_limit``: number of characters
-  ``imp_require``: cumulative scores [0.0-1.0] (only
   {lex,clex,div}rank)

Example
~~~~~~~

from (http://blog.recruit-tech.co.jp/2015/08/28/recruit_two_cx/)

Request
^^^^^^^

.. code:: sh

    curl http://127.0.0.1:8080/summarize\?sent_limit\=3\&text\=突然ですが、リクルートのリボンモデルを耳にしたことはあるでしょうか？...

Response (JSON format)
^^^^^^^^^^^^^^^^^^^^^^

.. code:: javascript

    {
      summary: [
        "リクルートが提供する多くのサービスが後に言及するカスタマーとクライアント双方のマッチングを実現するサービスと称される背景にはこのリボンモデルがあります。", 
        "そしてこのリボンモデルこそ、リクルートにおけるUXデザインそのもの、とも言えることができます。", 
        "リボンモデルは国際規格の普及によって生まれた構想ではないものの、リボンモデルの構造はカスタマーとクライアントの体験設計基盤とも捉えることができ、UXデザインの普及・浸透と足並を揃えるかのように組織内に醸成されていきました。"
      ],
      debug_info: {}
    }

Try with browser
~~~~~~~~~~~~~~~~

``http://<hostname>:<port>/static/test.html``

Python API
----------

Example (Continuous LexRank)
~~~~~~~~~~~~~~~~~~~~~~~~~~~~

.. code:: python

    from summpy.lexrank import summarize

    # ensure type(text) is unicode
    sentences, debug_info = summarize(
        text, sent_limit=5, continuous=True, debug=True
    )

    for sent in sentences:
        print sent.strip().encode(encoding)

For further details, see ``main`` part of ``summpy/lexrank.py`` or
``mcp_summ.py``.

References
----------

-  G. Erkan and D. Radev. LexRank: graph-based lexical centrality as
   salience in text summarization. J. Artif. Int. Res. 22(1), pages
   457-479, 2004.
   (`link <http://www.cs.cmu.edu/afs/cs/project/jair/pub/volume22/erkan04a-html/erkan04a.html>`__)
-  Q. Mei, J. Guo, and D. Radev. DivRank: the Interplay of Prestige and
   Diversity in Information Networks. In Proceedings of the 16th ACM
   SIGKDD International Conference on Knowledge Discovery and Data
   Mining (KDD '10), pages 1009-1018, 2010.
   (`link <http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.174.7982>`__)

-  H. Takamura and M. Okumura. Text Summarization Model Based on Maximum
   Coverage Problem and its Variant. In Proceedings of the 12th
   Conference of the European Chapter of the Association for
   Computational Linguistics (EACL '09), pages 781-789, 2009.
   (`link <http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.222.6945>`__)