Autumn (Bee) bee-san
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| function inverseDocumentFrequency(document){ | |
| // calculates the inverse document frequency of every sentence | |
| const words_without_stopwords = prettify(document); | |
| const unique_words_set = uniqueWords(words_without_stopwords); | |
| const sentences = document.split(".").map(item => item.trim()); | |
| sentences[0] = sentences[0].substring(146); | |
| const lengthOfDocuments = sentences.length; | |
| // prettifys each sentence so it doesn't have stopwords |
bee-san
/ manifest2.json
Created
August 27, 2018 13:31
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| { | |
| "manifest_version": 2, | |
| "name": "TL;DR - Summary of BBC news articles", | |
| "version": "1.0", | |
| "description": "This extension creates a summary of BBC news articles using TF*IDF", | |
| "icons": { | |
| "48": "icons/border-48.png" |
bee-san
/ manifest.json
Created
August 27, 2018 13:23
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| { | |
| "manifest_version": 2, | |
| "name": "TL;DR - Summary of BBC news articles", | |
| "version": "1.0", | |
| "description": "This extension creates a summary of BBC news articles using TF*IDF", | |
| "content_scripts": [ | |
| { | |
| "matches": ["*://www.bbc.co.uk/news/*"], | |
| "js": ["jquery.js", "tldr.js"] |
bee-san
/ JQuery3.js
Last active
August 27, 2018 13:21
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| // get all text from .story-body within p tags on a BBC news web article | |
| let $article = $('.story-body').find('p').text(); | |
| // insert text into body of document | |
| let insert = $('.story-body').prepend(TFIDF($article)); |
bee-san
/ jquery2.js
Last active
August 27, 2018 13:21
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| // get all text from .story-body within p tags on a BBC news web article | |
| let $article = $('.story-body').find('p').text(); |
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| let $article = $('.story-body').find('p') |
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| function TFIDF(documents){ | |
| // calculates TF*IDF | |
| const TFVals = termFrequency(documents); | |
| const IDFVals = inverseDocumentFrequency(documents); | |
| let TFidfDict = {}; | |
| for (const [key, value] of Object.entries(TFVals)){ | |
| if (key in IDFVals){ | |
| TFidfDict[key] = TFVals[key] * IDFVals[key]; |
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| function TFIDF(documents){ | |
| // calculates TF*IDF | |
| const TFVals = termFrequency(documents); | |
| const IDFVals = inverseDocumentFrequency(documents); | |
| let TFidfDict = {}; | |
| for (const [key, value] of Object.entries(TFVals)){ | |
| if (key in IDFVals){ | |
| TFidfDict[key] = TFVals[key] * IDFVals[key]; |
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| function TFIDF(documents){ | |
| // calculates TF*IDF | |
| const TFVals = termFrequency(documents); | |
| const IDFVals = inverseDocumentFrequency(documents); | |
| let TFidfDict = {}; | |
| for (const [key, value] of Object.entries(TFVals)){ | |
| if (key in IDFVals){ | |
| TFidfDict[key] = TFVals[key] * IDFVals[key]; |
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| function inverseDocumentFrequency(document){ | |
| // calculates the inverse document frequency of every sentence | |
| const words_without_stopwords = prettify(document); | |
| const unique_words_set = uniqueWords(words_without_stopwords); | |
| const sentences = document.split(".").map(item => item.trim()); | |
| sentences[0] = sentences[0].substring(146); | |
| const lengthOfDocuments = sentences.length; | |
| // prettifys each sentence so it doesn't have stopwords |