swagger.yaml

basePath: /api/v1.1.1/
host: explaintome-app.herokuapp.com
info:
  license:
    name: Apache 2.0
    url: 'http://www.apache.org/licenses/LICENSE-2.0.html'
  title: ExplainToMe API
  version: 1.1.1
  contact:
    email: jjangsangy@gmail.com
    name: Sang Han
    url: 'https://angel.co/sanghan'
  description: '[![travis](https://travis-ci.org/jjangsangy/ExplainToMe.svg?branch=master)](https://travis-ci.org/jjangsangy/ExplainToMe)

    [![licence](https://img.shields.io/pypi/l/coverage.svg)](https://github.com/jjangsangy/ExplainToMe/blob/master/LICENSE)


    ## Automatic Web Article Summarizer


    ![image](https://github.com/jjangsangy/ExplainToMe/raw/master/static/front.jpg)


    [![Deploy](https://www.herokucdn.com/deploy/button.svg)](https://heroku.com/deploy)


    # What is it?


    `Explain To Me` is a automatic text summarizer, that utilizes

    [TextRank](http://web.eecs.umich.edu/~mihalcea/papers/mihalcea.emnlp04.pdf),

    a graph based algorithm to scans through the contents of a website to

    extract a concise machine generated summary. The methodology is similar

    to the way search engines return the most relevant web pages from a

    users search query.

    '
consumes: [application/json]
definitions:
  Article:
    description: Article Summary Details
    properties:
      lang:
        description: Article language
        enum: [en zn-ch zn-tw de pt fr it ja ko es]
        example: en
        readOnly: true
        type: string
      max_sent: 
        default: 10
        description: Maximum number of sentences returned
        format: integer
        maximum: 100
        minimum: 1
        type: integer
      url:
        description: Web Article URL
        example: 'https://www.technologyreview.com/s/513696/deep-learning/'
        format: uri
        type: string
    required: [url]
    title: Article
    type: object
  Error:
    title: Error
    properties:
      code:
        enum: [bad_request unauthorized forbidden not_found method_not_allowed conflict
            precondition_failed too_many_requests internal_server_error unavailable]
        type: string
      status:
        example: 404
        format: int32
        maximum: 600
        minimum: 200
        type: integer
      type: {type: string}
  Error404:
    allOf:
    - {$ref: '#/definitions/Error'}
    title: Error404
    type: object
  Extract:
    description: Article Sentence
    title: Extracted sentence
    type: string
  Image:
    description: Extracted image metadata
    properties:
      height: {format: int32, type: integer}
      type: {type: string}
      url: {format: uri, type: string}
      width: {format: int32, type: integer}
    title: Image
    type: object
  MetaData:
    description: WebPage Metadata
    properties:
      authors:
        items: {type: string}
        type: array
      domain: {format: uri, type: string}
      image: {$ref: '#/definitions/Image'}
      meta: {$ref: '#/definitions/MetaMeta'}
      opengraph: {type: object}
      publish_date: {format: date-time, type: string}
      tags:
        items: {type: string}
        type: array
      title: {type: string}
    title: Metadata
    type: object
  MetaMeta:
    description: Metadata Metadata
    properties:
      canonical: {format: uri, type: string}
      favicon: {format: uri, type: string}
    title: MetaMeta
  Summary:
    allOf:
    - {$ref: '#/definitions/Article'}
    - properties:
        meta: {$ref: '#/definitions/MetaData'}
        summary:
          items: {$ref: '#/definitions/Extract'}
          minItems: 0
          type: array
      type: object
    description: Article Summary and Metadata
    title: Summary
    type: object
paths:
  /summary:
    post:
      parameters:
      - in: body
        name: Request
        schema: {$ref: '#/definitions/Article'}
      responses:
        200:
          description: Response Summary
          examples:
            application/json:
              max_sent: 10
              meta:
                authors: []
                domain: www.technologyreview.com
                image: {height: 459, type: image, url: 'https://d267cvn3rvuq91.cloudfront.net/i/images/building.a.brain_.timelinex519_0.jpg?sw=373',
                  width: 373}
                meta: {canonical: 'https://www.technologyreview.com/s/513696/deep-learning/',
                  description: 'With massive amounts of computational power, machines
                    can now recognize objects and translate speech in real time. Artificial
                    intelligence is finally getting smart.', favicon: '', keywords: '',
                  lang: en}
                opengraph: {description: 'With massive amounts of computational power,
                    machines can now recognize objects and translate speech in real
                    time. Artificial intelligence is finally getting smart.', image: '392',
                  site_name: MIT Technology Review, title: 'Is Artificial Intelligence
                    Finally Coming into Its Own?', type: article, updated_time: '2016-03-29T11:11:13-04:00',
                  url: 'https://www.technologyreview.com/s/513696/deep-learning/'}
                publish_date: null
                tags: []
                title: Is Artificial Intelligence Finally Coming into Its Own?
              summary: [Kurzweil was attracted not just by Googles computing resources
                  but also by the startling progress the company has made in a branch
                  of AI called deep learning., 'The software learns, in a very real
                  sense, to recognize patterns in digital representations of sounds,
                  images, and other data.', 'Last June, a Google deep-learning system
                  that had been shown 10 million images from YouTube videos proved
                  almost twice as good as any previous image recognition effort at
                  identifying objects such as cats.', 'Extending deep learning into
                  applications beyond speech and image recognition will require more
                  conceptual and software breakthroughs, not to mention many more
                  advances in processing power.', Programmers would train a neural
                  network to detect an object or phoneme by blitzing the network with
                  digitized versions of images containing those objects or sound waves
                  containing those phonemes., 'The eventual goal of this training
                  was to get the network to consistently recognize the patterns in
                  speech or sets of images that we humans know as, say, the phoneme
                  da or the image of a dog.', And complex speech or image recognition
                  required more computer power than was then available., A team led
                  by Stanford computer science professor Andrew Ng and Google Fellow
                  Jeff Dean showed the system images from 10 million randomly selected
                  YouTube videos., 'Training the many layers of virtual neurons in
                  the experiment took 16,000 computer processorsathe kind of computing
                  infrastructure that Google has developed for its search engine and
                  other services.', 'Though Kurzweila vision is still years from reality,
                  deep learning is likely to spur other applications beyond speech
                  and image recognition in the nearer term.']
              url: https://www.technologyreview.com/s/513696/deep-learning/
          schema: {$ref: '#/definitions/Summary'}
        404:
          description: Resource Not Found
          schema: {$ref: '#/definitions/Error404'}
        default:
          description: Unexpected error
          examples:
            application/json: {code: not_found, status: 404, type: error}
          schema: {$ref: '#/definitions/Error'}
      tags: [Article]
produces: [application/json]
schemes: [https]
swagger: '2.0'
tags:
- {description: URL String, name: URL}
- {description: Document, name: Article}
- description: Process of reducing a text document that retains original message.
  externalDocs: {description: Wikipedia, url: 'https://en.wikipedia.org/wiki/Automatic_summarization'}
  name: Summary