\text{Precision} = \frac{\text{Total number of overlapping unigrams}}{\text{Total number of unigrams in the candidate summary}}

\text{Recall} = \frac{\text{Total number of overlapping unigrams}}{\text{Total number of unigrams in the reference summary}}

F_{\text{mean}} = \frac{PR}{\alpha P + 
(1-\alpha)R}

\text{Penalty} = \gamma * \big[\frac{\text{chunk}}{\text{matching unigrams}}\big]^\beta

If the candidate summary is exactly similar to the reference summary, which is an ideal case, we'll only have one chunk because all the words in the candidate summary will map to all the words in the reference summary consecutively.

<p>If the candidate summary is exactly similar to the reference summary, which is an ideal case, we’ll only have one chunk because all the words in the candidate summary will map to all the words in the reference summary consecutively.</p>


What would be the chunk size in case of candidate summary is exactly similar to the reference summary?

<p>What would be the chunk size in case of candidate summary is exactly similar to the reference summary?</p>


import nltk
nltk.download('wordnet')

reference_summary = [['Machine', 'learning', 'is', 'a', 'subset', 'of', 'artificial', 'intelligence']]
candidate_summary = ['Machine', 'learning', 'is', 'seen', 'as', 'a', 'subset', 'of', 'artificial', 'intelligence']

METEORscore = nltk.translate.meteor_score.meteor_score(reference_summary, candidate_summary)
print(METEORscore)

import React from 'react';
import { shallow, configure } from 'enzyme';
import Adapter from 'enzyme-adapter-react-16';

configure({ adapter: new Adapter() });

import HelloWorld from './app';

var TestResult = function() {
    this.succeeded = false;
    this.reason = "";
    this.input = "";
    this.expected_output = "";
    this.actual_output = "";
}

export const executeTests = function() {

  var results = [];

  result = new TestResult();
  result.input = 'HelloWorld Component';
  result.expected_output = "span containing text 'Hello World'"

  let wrapper = shallow(<HelloWorld />);

  // Call your Challenge function here.

  let type = wrapper.type();
  let testSuccessful = true;
  let failureReason;

  if (type !== 'span') {
    testSuccessful = false;
    failureReason = "You need to render exactly one span HTML element";
  } else if (wrapper.props().children != "Hello World") {
     testSuccessful = false;
     failureReason = "You have rendered wrong message in your span element";
  }

  result.actual_output = wrapper.html();

  if (testSuccessful) {
    result.succeeded = true;
    result.reason = "Succeeded"
  } else {
    result.succeeded = false;
    result.reason = failureReason;
  }

  results.push(result);

  return results;
}


How does METEOR evaluation metric calculate the similarity score?

What is METEOR?

Calculating the METEOR score

Calculate the unigram precision and recall

Compute the F-score

Compute chunk penalty

Calculate the METEOR score

Code example

Code explanation