textrank

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README

TextRank on Go

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This source code is an implementation of textrank algorithm, under MIT licence.
The minimum requred Go version is 1.8.


MOTIVATION

If there was a program what could rank book size text's words, phrases and sentences continuously on multiple threads and it would be opened to modifing by objects, written in a simple, secure, static language and if it would be very well documented... Now, here it is.

DEMO

The following link Recona is a simple, pre-programmed a.i. what uses this library to ranking raw texts. It visualizes how ranking works and it represents how it could be used for different purposes: Recona.app

FEATURES

  • Find the most important phrases.
  • Find the most important words.
  • Find the most important N sentences.
    • Importance by phrase weights.
    • Importance by word occurrence.
  • Find the first N sentences, start from Xth sentence.
  • Find sentences by phrase chains ordered by position in text.
  • Access to the whole ranked data.
  • Support more languages.
  • Algorithm for weighting can be modified by interface implementation.
  • Parser can be modified by interface implementation.
  • Multi thread support.

INSTALL

You can install TextRank by Go's get:

go get github.com/DavidBelicza/TextRank

TextRank uses the DEP as vendoring tool, so the required dependencies are versioned under the vendor folder. The exact version number defined in the Gopkg.toml. If you want to reinstall the dependencies, use the DEP functions: flush the vendor folder and run:

dep ensure

DOCKER

Using Docker to TextRank isn't necessary, it's just an option.

Build image from the repository's root directory:

docker build -t go_text_rank_image .

Create container from the image:

docker run -dit --name textrank go_text_rank_image:latest

Run the go test -v . code inside the container:

docker exec -i -t textrank go test -v .

Stop, start or remove the container:

  • docker stop textrank
  • docker start textrank
  • docker rm textrank

HOW DOES IT WORK

Too see how does it work, the easiest way is to use the sample text. Sample text can be found in the textrank_test.go file at this line. It's a short size text about Gnome Shell.

  • TextRank reads the text,
    • parse it,
    • remove the unnecessary stop words,
    • tokenize it
  • and counting the occurrence of the words and phrases
  • and then it starts weighting
    • by the occurrence of words and phrases and their relations.
  • After weights are done, TextRank normalize weights to between 1 and 0.
  • Then the different finder methods capable to find the most important words, phrases or sentences.

The most important phrases from the sample text are:

Phrase Occurrence Weight
gnome - shell 5 1
extension - gnome 3 0.50859946
icons - tray 3 0.49631447
gnome - caffeine 2 0.27027023

The gnome is the most often used word in this text and shell is also used multiple times. Two of them are used together as a phrase 5 times. This is the highest occurrence in this text, so this is the most important phrase.

The following two important phrases have same occurrence 3, however they are not equal. This is because the extension gnome phrase contains the word gnome, the most popular word in the text, and it increases the phrase's weight. It increases the weight of any word what is related to it, but not too much to overcome other important phrases what don't contain the gnome word.

The exact algorithm can be found in the algorithm.go file at this line.

TEXTRANK OR AUTOMATIC SUMMARIZATION

Automatic summarization is the process of reducing a text document with a computer program in order to create a summary that retains the most important points of the original document. Technologies that can make a coherent summary take into account variables such as length, writing style and syntax. Automatic data summarization is part of machine learning and data mining. The main idea of summarization is to find a representative subset of the data, which contains the information of the entire set. Summarization technologies are used in a large number of sectors in industry today. - Wikipedia

EXAMPLES

Find the most important phrases

This is the most basic and simplest usage of textrank.

package main

import (
	"fmt"
	
	"github.com/DavidBelicza/TextRank"
)

func main() {
	rawText := "Your long raw text, it could be a book. Lorem ipsum..."
	// TextRank object
	tr := textrank.NewTextRank()
	// Default Rule for parsing.
	rule := textrank.NewDefaultRule()
	// Default Language for filtering stop words.
	language := textrank.NewDefaultLanguage()
	// Default algorithm for ranking text.
	algorithmDef := textrank.NewDefaultAlgorithm()

	// Add text.
	tr.Populate(rawText, language, rule)
	// Run the ranking.
	tr.Ranking(algorithmDef)

	// Get all phrases by weight.
	rankedPhrases := textrank.FindPhrases(tr)

	// Most important phrase.
	fmt.Println(rankedPhrases[0])
	// Second important phrase.
	fmt.Println(rankedPhrases[1])
}

All possible pre-defined finder queries

After ranking, the graph contains a lot of valuable data. There are functions in textrank package what contains logic to retrieve those data from the graph.

package main

import (
	"fmt"
	
	"github.com/DavidBelicza/TextRank"
)

func main() {
	rawText := "Your long raw text, it could be a book. Lorem ipsum..."
	// TextRank object
	tr := textrank.NewTextRank()
	// Default Rule for parsing.
	rule := textrank.NewDefaultRule()
	// Default Language for filtering stop words.
	language := textrank.NewDefaultLanguage()
	// Default algorithm for ranking text.
	algorithmDef := textrank.NewDefaultAlgorithm()

	// Add text.
	tr.Populate(rawText, language, rule)
	// Run the ranking.
	tr.Ranking(algorithmDef)

	// Get all phrases order by weight.
	rankedPhrases := textrank.FindPhrases(tr)
	// Most important phrase.
	fmt.Println(rankedPhrases[0])

	// Get all words order by weight.
	words := textrank.FindSingleWords(tr)
	// Most important word.
	fmt.Println(words[0])

	// Get the most important 10 sentences. Importance by phrase weights.
	sentences := textrank.FindSentencesByRelationWeight(tr, 10)
	// Found sentences
	fmt.Println(sentences)

	// Get the most important 10 sentences. Importance by word occurrence.
	sentences = textrank.FindSentencesByWordQtyWeight(tr, 10)
	// Found sentences
	fmt.Println(sentences)

	// Get the first 10 sentences, start from 5th sentence.
	sentences = textrank.FindSentencesFrom(tr, 5, 10)
	// Found sentences
	fmt.Println(sentences)

	// Get sentences by phrase/word chains order by position in text.
	sentencesPh := textrank.FindSentencesByPhraseChain(tr, []string{"gnome", "shell", "extension"})
	// Found sentence.
	fmt.Println(sentencesPh[0])
}

Access to everything

After ranking, the graph contains a lot of valuable data. The GetRank function allows access to the graph and every data can be retrieved from this structure.

package main

import (
	"fmt"
	
	"github.com/DavidBelicza/TextRank"
)

func main() {
	rawText := "Your long raw text, it could be a book. Lorem ipsum..."
	// TextRank object
	tr := textrank.NewTextRank()
	// Default Rule for parsing.
	rule := textrank.NewDefaultRule()
	// Default Language for filtering stop words.
	language := textrank.NewDefaultLanguage()
	// Default algorithm for ranking text.
	algorithmDef := textrank.NewDefaultAlgorithm()

	// Add text.
	tr.Populate(rawText, language, rule)
	// Run the ranking.
	tr.Ranking(algorithmDef)

	// Get the rank graph.
	rankData := tr.GetRankData()

	// Get word ID by token/word.
	wordId := rankData.WordValID["gnome"]

	// Word's weight.
	fmt.Println(rankData.Words[wordId].Weight)
	// Word's quantity/occurrence.
	fmt.Println(rankData.Words[wordId].Qty)
	// All sentences what contain the this word.
	fmt.Println(rankData.Words[wordId].SentenceIDs)
	// All other words what are related to this word on left side.
	fmt.Println(rankData.Words[wordId].ConnectionLeft)
	// All other words what are related to this word on right side.
	fmt.Println(rankData.Words[wordId].ConnectionRight)
	// The node of this word, it contains the related words and the relation weight.
	fmt.Println(rankData.Relation.Node[wordId])
}

Adding text continuously

It is possibe to add more text after another texts already have been added. The Ranking function can merge these multiple texts and it can recalculate the weights and all related data.

package main

import (
	"fmt"
	
	"github.com/DavidBelicza/TextRank"
)

func main() {
	rawText := "Your long raw text, it could be a book. Lorem ipsum..."
	// TextRank object
	tr := textrank.NewTextRank()
	// Default Rule for parsing.
	rule := textrank.NewDefaultRule()
	// Default Language for filtering stop words.
	language := textrank.NewDefaultLanguage()
	// Default algorithm for ranking text.
	algorithmDef := textrank.NewDefaultAlgorithm()

	// Add text.
	tr.Populate(rawText, language, rule)
	// Run the ranking.
	tr.Ranking(algorithmDef)

	rawText2 := "Another book or article..."
	rawText3 := "Third another book or article..."

	// Add text to the previously added text.
	tr.Populate(rawText2, language, rule)
	// Add text to the previously added text.
	tr.Populate(rawText3, language, rule)
	// Run the ranking to the whole composed text.
	tr.Ranking(algorithmDef)

	// Get all phrases by weight.
	rankedPhrases := textrank.FindPhrases(tr)

	// Most important phrase.
	fmt.Println(rankedPhrases[0])
	// Second important phrase.
	fmt.Println(rankedPhrases[1])
}

Using different algorithm to ranking text

There are two algorithm has implemented, it is possible to write custom algorithm by Algorithm interface and use it instead of defaults.

package main

import (
	"fmt"
	
	"github.com/DavidBelicza/TextRank"
)

func main() {
	rawText := "Your long raw text, it could be a book. Lorem ipsum..."
	// TextRank object
	tr := textrank.NewTextRank()
	// Default Rule for parsing.
	rule := textrank.NewDefaultRule()
	// Default Language for filtering stop words.
	language := textrank.NewDefaultLanguage()
	// Using a little bit more complex algorithm to ranking text.
	algorithmChain := textrank.NewChainAlgorithm()

	// Add text.
	tr.Populate(rawText, language, rule)
	// Run the ranking.
	tr.Ranking(algorithmChain)

	// Get all phrases by weight.
	rankedPhrases := textrank.FindPhrases(tr)

	// Most important phrase.
	fmt.Println(rankedPhrases[0])
	// Second important phrase.
	fmt.Println(rankedPhrases[1])
}

Using multiple graphs

Graph ID exists because it is possible run multiple independent text ranking processes.

package main

import (
	"fmt"
	
	"github.com/DavidBelicza/TextRank"
)

func main() {
	rawText := "Your long raw text, it could be a book. Lorem ipsum..."
	// 1th TextRank object
	tr1 := textrank.NewTextRank()
	// Default Rule for parsing.
	rule := textrank.NewDefaultRule()
	// Default Language for filtering stop words.
	language := textrank.NewDefaultLanguage()
	// Default algorithm for ranking text.
	algorithmDef := textrank.NewDefaultAlgorithm()

	// Add text.
	tr1.Populate(rawText, language, rule)
	// Run the ranking.
	tr1.Ranking(algorithmDef)

	// 2nd TextRank object
	tr2 := textrank.NewTextRank()

	// Using a little bit more complex algorithm to ranking text.
	algorithmChain := textrank.NewChainAlgorithm()

	// Add text to the second graph.
	tr2.Populate(rawText, language, rule)
	// Run the ranking on the second graph.
	tr2.Ranking(algorithmChain)

	// Get all phrases by weight from first graph.
	rankedPhrases := textrank.FindPhrases(tr1)

	// Most important phrase from first graph.
	fmt.Println(rankedPhrases[0])
	// Second important phrase from first graph.
	fmt.Println(rankedPhrases[1])

	// Get all phrases by weight from second graph.
	rankedPhrases2 := textrank.FindPhrases(tr2)

	// Most important phrase from second graph.
	fmt.Println(rankedPhrases2[0])
	// Second important phrase from second graph.
	fmt.Println(rankedPhrases2[1])
}

Using different non-English languages

Engish is used by default but it is possible to add any language. To use other languages a stop word list is required what you can find here: https://github.com/stopwords-iso

package main

import (
	"fmt"
	
	"github.com/DavidBelicza/TextRank"
)

func main() {
	rawText := "Your long raw text, it could be a book. Lorem ipsum..."
	// TextRank object
	tr := textrank.NewTextRank()
	// Default Rule for parsing.
	rule := textrank.NewDefaultRule()
	// Default Language for filtering stop words.
	language := textrank.NewDefaultLanguage()

	// Add Spanish stop words (just some example).
	language.SetWords("es", []string{"uno", "dos", "tres", "yo", "es", "eres"})
	// Active the Spanish.
	language.SetActiveLanguage("es")

	// Default algorithm for ranking text.
	algorithmDef := textrank.NewDefaultAlgorithm()

	// Add text.
	tr.Populate(rawText, language, rule)
	// Run the ranking.
	tr.Ranking(algorithmDef)

	// Get all phrases by weight.
	rankedPhrases := textrank.FindPhrases(tr)

	// Most important phrase.
	fmt.Println(rankedPhrases[0])
	// Second important phrase.
	fmt.Println(rankedPhrases[1])
}

Asynchronous usage by goroutines

It is thread safe. Independent graphs can receive texts in the same time and can be extended by more text also in the same time.

package main

import (
	"fmt"
	"time"
	
	"github.com/DavidBelicza/TextRank"
)

func main() {
	// A flag when program has to stop.
	stopProgram := false
	// Channel.
	stream := make(chan string)
	// TextRank object.
	tr := textrank.NewTextRank()

	// Open new thread/routine
	go func(tr *textrank.TextRank) {
		// 3 texts.
		rawTexts := []string{
			"Very long text...",
			"Another very long text...",
			"Second another very long text...",
		}

		// Add 3 texts to the stream channel, one by one.
		for _, rawText := range rawTexts {
			stream <- rawText
		}
	}(tr)

	// Open new thread/routine
	go func() {
		// Counter how many times texts added to the ranking.
		i := 1

		for {
			// Get text from stream channel when it got a new one.
			rawText := <-stream

			// Default Rule for parsing.
			rule := textrank.NewDefaultRule()
			// Default Language for filtering stop words.
			language := textrank.NewDefaultLanguage()
			// Default algorithm for ranking text.
			algorithm := textrank.NewDefaultAlgorithm()

			// Add text.
			tr.Populate(rawText, language, rule)
			// Run the ranking.
			tr.Ranking(algorithm)

			// Set stopProgram flag to true when all 3 text have been added.
			if i == 3 {
				stopProgram = true
			}

			i++
		}
	}()

	// The main thread has to run while go-routines run. When stopProgram is
	// true then the loop has finish.
	for !stopProgram {
		time.Sleep(time.Second * 1)
	}

	// Most important phrase.
	phrases := textrank.FindPhrases(tr)
	// Second important phrase.
	fmt.Println(phrases[0])
}

A SIMPLE VISUAL REPRESENTATION

The below image is a representation how works the simplest text ranking algorithm. This algorithm can be replaced by an another one by inject different Algorithm interface implementation.

Documentation

Overview

Package textrank is an implementation of Text Rank algorithm in Go with extendable features (automatic summarization, phrase extraction). It supports multithreading by goroutines. The package is under The MIT Licence.

MOTIVATION

If there was a program what could rank book size text's words, phrases and sentences continuously on multiple threads and it would be opened to modifing by objects, written in a simple, secure, static language and if it would be very well documented... Now, here it is.

FEATURES

- Find the most important phrases. - Find the most important words. - Find the most important N sentences. - Importance by phrase weights. - Importance by word occurrence. - Find the first N sentences, start from Xth sentence. - Find sentences by phrase chains ordered by position in text. - Access to the whole ranked data. - Support more languages. - Algorithm for weighting can be modified by interface implementation. - Parser can be modified by interface implementation. - Multi thread support.

EXAMPLES

Find the most important phrases:

This is the most basic and simplest usage of textrank. 

package main

import (
	"fmt"

	"github.com/DavidBelicza/TextRank"
)

func main() {
	rawText := "Your long raw text, it could be a book. Lorem ipsum..."
	// TextRank object
	tr := textrank.NewTextRank()
	// Default Rule for parsing.
	rule := textrank.NewDefaultRule()
	// Default Language for filtering stop words.
	language := textrank.NewDefaultLanguage()
	// Default algorithm for ranking text.
	algorithmDef := textrank.NewDefaultAlgorithm()

	// Add text.
	tr.Populate(rawText, language, rule)
	// Run the ranking.
	tr.Ranking(algorithmDef)

	// Get all phrases by weight.
	rankedPhrases := textrank.FindPhrases(tr)

	// Most important phrase.
	fmt.Println(rankedPhrases[0])
	// Second important phrase.
	fmt.Println(rankedPhrases[1])
}

All possible pre-defined finder queries:

After ranking, the graph contains a lot of valuable data. There are functions in textrank package what contains logic to retrieve those data from the graph. 

package main

import (
	"fmt"

	"github.com/DavidBelicza/TextRank"
)

func main() {
	rawText := "Your long raw text, it could be a book. Lorem ipsum..."
	// TextRank object
	tr := textrank.NewTextRank()
	// Default Rule for parsing.
	rule := textrank.NewDefaultRule()
	// Default Language for filtering stop words.
	language := textrank.NewDefaultLanguage()
	// Default algorithm for ranking text.
	algorithmDef := textrank.NewDefaultAlgorithm()

	// Add text.
	tr.Populate(rawText, language, rule)
	// Run the ranking.
	tr.Ranking(algorithmDef)

	// Get all phrases order by weight.
	rankedPhrases := textrank.FindPhrases(tr)
	// Most important phrase.
	fmt.Println(rankedPhrases[0])

	// Get all words order by weight.
	words := textrank.FindSingleWords(tr)
	// Most important word.
	fmt.Println(words[0])

	// Get the most important 10 sentences. Importance by phrase weights.
	sentences := textrank.FindSentencesByRelationWeight(tr, 10)
	// Found sentences
	fmt.Println(sentences)

	// Get the most important 10 sentences. Importance by word occurrence.
	sentences = textrank.FindSentencesByWordQtyWeight(tr, 10)
	// Found sentences
	fmt.Println(sentences)

	// Get the first 10 sentences, start from 5th sentence.
	sentences = textrank.FindSentencesFrom(tr, 5, 10)
	// Found sentences
	fmt.Println(sentences)

	// Get sentences by phrase/word chains order by position in text.
	sentencesPh := textrank.FindSentencesByPhraseChain(tr, []string{"gnome", "shell", "extension"})
	// Found sentence.
	fmt.Println(sentencesPh[0])
}

Access to everything

After ranking, the graph contains a lot of valuable data. The GetRank function allows access to the graph and every data can be retrieved from this structure. 

package main

import (
	"fmt"

	"github.com/DavidBelicza/TextRank"
)

func main() {
	rawText := "Your long raw text, it could be a book. Lorem ipsum..."
	// TextRank object
	tr := textrank.NewTextRank()
	// Default Rule for parsing.
	rule := textrank.NewDefaultRule()
	// Default Language for filtering stop words.
	language := textrank.NewDefaultLanguage()
	// Default algorithm for ranking text.
	algorithmDef := textrank.NewDefaultAlgorithm()

	// Add text.
	tr.Populate(rawText, language, rule)
	// Run the ranking.
	tr.Ranking(algorithmDef)

	// Get the rank graph.
	rankData := tr.GetRankData()

	// Get word ID by token/word.
	wordId := rankData.WordValID["gnome"]

	// Word's weight.
	fmt.Println(rankData.Words[wordId].Weight)
	// Word's quantity/occurrence.
	fmt.Println(rankData.Words[wordId].Qty)
	// All sentences what contain the this word.
	fmt.Println(rankData.Words[wordId].SentenceIDs)
	// All other words what are related to this word on left side.
	fmt.Println(rankData.Words[wordId].ConnectionLeft)
	// All other words what are related to this word on right side.
	fmt.Println(rankData.Words[wordId].ConnectionRight)
	// The node of this word, it contains the related words and the
	// relation weight.
	fmt.Println(rankData.Relation.Node[wordId])
}

Adding text continuously:

It is possibe to add more text after another texts already have been added. The Ranking function can merge these multiple texts and it can recalculate the weights and all related data. 

package main

import (
	"fmt"

	"github.com/DavidBelicza/TextRank"
)

func main() {
	rawText := "Your long raw text, it could be a book. Lorem ipsum..."
	// TextRank object
	tr := textrank.NewTextRank()
	// Default Rule for parsing.
	rule := textrank.NewDefaultRule()
	// Default Language for filtering stop words.
	language := textrank.NewDefaultLanguage()
	// Default algorithm for ranking text.
	algorithmDef := textrank.NewDefaultAlgorithm()

	// Add text.
	tr.Populate(rawText, language, rule)
	// Run the ranking.
	tr.Ranking(algorithmDef)

	rawText2 := "Another book or article..."
	rawText3 := "Third another book or article..."

	// Add text to the previously added text.
	tr.Populate(rawText2, language, rule)
	// Add text to the previously added text.
	tr.Populate(rawText3, language, rule)
	// Run the ranking to the whole composed text.
	tr.Ranking(algorithmDef)

	// Get all phrases by weight.
	rankedPhrases := textrank.FindPhrases(tr)

	// Most important phrase.
	fmt.Println(rankedPhrases[0])
	// Second important phrase.
	fmt.Println(rankedPhrases[1])
}

Using different algorithm to ranking text:

There are two algorithm has implemented, it is possible to write custom algorithm by Algorithm interface and use it instead of defaults. 

package main

import (
	"fmt"

	"github.com/DavidBelicza/TextRank"
)

func main() {
	rawText := "Your long raw text, it could be a book. Lorem ipsum..."
	// TextRank object
	tr := textrank.NewTextRank()
	// Default Rule for parsing.
	rule := textrank.NewDefaultRule()
	// Default Language for filtering stop words.
	language := textrank.NewDefaultLanguage()
	// Using a little bit more complex algorithm to ranking text.
	algorithmChain := textrank.NewChainAlgorithm()

	// Add text.
	tr.Populate(rawText, language, rule)
	// Run the ranking.
	tr.Ranking(algorithmChain)

	// Get all phrases by weight.
	rankedPhrases := textrank.FindPhrases(tr)

	// Most important phrase.
	fmt.Println(rankedPhrases[0])
	// Second important phrase.
	fmt.Println(rankedPhrases[1])
}

Using multiple graphs:

Graph ID exists because it is possible run multiple independent text ranking processes. 

package main

import (
	"fmt"

	"github.com/DavidBelicza/TextRank"
)

func main() {
	rawText := "Your long raw text, it could be a book. Lorem ipsum..."
	// 1th TextRank object
	tr1 := textrank.NewTextRank()
	// Default Rule for parsing.
	rule := textrank.NewDefaultRule()
	// Default Language for filtering stop words.
	language := textrank.NewDefaultLanguage()
	// Default algorithm for ranking text.
	algorithmDef := textrank.NewDefaultAlgorithm()

	// Add text.
	tr1.Populate(rawText, language, rule)
	// Run the ranking.
	tr1.Ranking(algorithmDef)

	// 2nd TextRank object
	tr2 := textrank.NewTextRank()

	// Using a little bit more complex algorithm to ranking text.
	algorithmChain := textrank.NewChainAlgorithm()

	// Add text to the second graph.
	tr2.Populate(rawText, language, rule)
	// Run the ranking on the second graph.
	tr2.Ranking(algorithmChain)

	// Get all phrases by weight from first graph.
	rankedPhrases := textrank.FindPhrases(tr1)

	// Most important phrase from first graph.
	fmt.Println(rankedPhrases[0])
	// Second important phrase from first graph.
	fmt.Println(rankedPhrases[1])

	// Get all phrases by weight from second graph.
	rankedPhrases2 := textrank.FindPhrases(tr2)

	// Most important phrase from second graph.
	fmt.Println(rankedPhrases2[0])
	// Second important phrase from second graph.
	fmt.Println(rankedPhrases2[1])
}

Using different non-English languages:

Engish is used by default but it is possible to add any language. To use other languages a stop word list is required what you can find here: https://github.com/stopwords-iso 

package main

import (
	"fmt"

	"github.com/DavidBelicza/TextRank"
)

func main() {
	rawText := "Your long raw text, it could be a book. Lorem ipsum..."
	// TextRank object
	tr := textrank.NewTextRank()
	// Default Rule for parsing.
	rule := textrank.NewDefaultRule()
	// Default Language for filtering stop words.
	language := textrank.NewDefaultLanguage()

	// Add Spanish stop words (just some example).
	language.SetWords("es", []string{"uno", "dos", "tres", "yo", "es", "eres"})
	// Active the Spanish.
	language.SetActiveLanguage("es")

	// Default algorithm for ranking text.
	algorithmDef := textrank.NewDefaultAlgorithm()

	// Add text.
	tr.Populate(rawText, language, rule)
	// Run the ranking.
	tr.Ranking(algorithmDef)

	// Get all phrases by weight.
	rankedPhrases := textrank.FindPhrases(tr)

	// Most important phrase.
	fmt.Println(rankedPhrases[0])
	// Second important phrase.
	fmt.Println(rankedPhrases[1])
}

Asynchronous usage by goroutines:

It is thread safe. Independent graphs can receive texts in the same time and can be extended by more text also in the same time. 

package main

import (
	"fmt"
	"time"

	"github.com/DavidBelicza/TextRank"
)

func main() {
	// A flag when program has to stop.
	stopProgram := false
	// Channel.
	stream := make(chan string)
	// TextRank object.
	tr := textrank.NewTextRank()

	// Open new thread/routine
	go func(tr *textrank.TextRank) {
		// 3 texts.
		rawTexts := []string{
			"Very long text...",
			"Another very long text...",
			"Second another very long text...",
		}

		// Add 3 texts to the stream channel, one by one.
		for _, rawText := range rawTexts {
			stream <- rawText
		}
	}(tr)

	// Open new thread/routine
	go func() {
		// Counter how many times texts added to the ranking.
		i := 1

		for {
			// Get text from stream channel when it got a new one.
			rawText := <-stream

			// Default Rule for parsing.
			rule := textrank.NewDefaultRule()
			// Default Language for filtering stop words.
			language := textrank.NewDefaultLanguage()
			// Default algorithm for ranking text.
			algorithm := textrank.NewDefaultAlgorithm()

			// Add text.
			tr.Populate(rawText, language, rule)
			// Run the ranking.
			tr.Ranking(algorithm)

			// Set stopProgram flag to true when all 3 text have been added.
			if i == 3 {
				stopProgram = true
			}

			i++
		}
	}()

	// The main thread has to run while go-routines run. When stopProgram is
	// true then the loop has finish.
	for !stopProgram {
		time.Sleep(time.Second * 1)
	}

	// Most important phrase.
	phrases := textrank.FindPhrases(tr)
	// Second important phrase.
	fmt.Println(phrases[0])
}

Index

Constants

This section is empty.

Variables

This section is empty.

Functions

func FindPhrases 

func FindPhrases(textRank *TextRank) []rank.Phrase

FindPhrases function retrieves a slice of Phrase structures by TextRank object. The return value contains the sorted phrases with IDs, words, weights and quantities by weight from 1 to 0. Weight is calculated from quantities of relation between two words. A single phrase is from two words - not less and more. (But it's possible to find chain of phrases by FindSentencesByPhraseChain function.)

func FindSentencesByPhraseChain 

func FindSentencesByPhraseChain(
	textRank *TextRank,
	phrases []string,
) []rank.Sentence

FindSentencesByPhraseChain function retrieves a slice of Sentence structures by TextRank object and slice of phrases. The return value contains the ID of the sentence and the sentence text itself. The slice is sorted by weight of word quantities from 1 to 0.

textRank TextRank is the object of the TextRank.

phrases []string is a slice of phrases. A single phrase is from two words, so when the slice contains 3 words the inner method will search for two phrases. The search algorithm seeks for "len(phrases)!". In case of three item the possible combination is 3 factorial (3!) = 3 * 2 * 1. 

rawText := "Long raw text, lorem ipsum..."
rule := NewDefaultRule()
language := NewDefaultLanguage()
algorithm := NewDefaultAlgorithm()

Append(rawText, language, rule, 1)
Ranking(1, algorithm)

FindSentencesByPhraseChain(1, []string{
    "captain",
    "james",
    "kirk",
})

The above code searches for captain james kirk, captain kirk james, james kirk captain, james captain kirk, kirk james captain and james kirk captain combinations in the graph. The 3 of words have to be related to each other in the same sentence but the search algorithm ignores the stop words. So if there is a sentence "James Kirk is the Captain of the Enterprise." the sentence will be returned because the words "is" and "the" are stop words.

func FindSentencesByRelationWeight 

func FindSentencesByRelationWeight(
	textRank *TextRank,
	limit int,
) []rank.Sentence

FindSentencesByRelationWeight function retrieves a slice of Sentence structures by TextRank object. The return value contains the ID of the sentence and the sentence text itself. The slice is sorted by weight of phrases from 1 to 0.

func FindSentencesByWordQtyWeight 

func FindSentencesByWordQtyWeight(
	textRank *TextRank,
	limit int,
) []rank.Sentence

FindSentencesByWordQtyWeight function retrieves a slice of Sentence structures by TextRank object. The return value contains the ID of the sentence and the sentence text itself. The slice is sorted by weight of word quantities from 1 to 0.

func FindSentencesFrom 

func FindSentencesFrom(
	textRank *TextRank,
	sentenceID int,
	limit int,
) []rank.Sentence

FindSentencesFrom function retrieves a slice of Sentence structures by TextRank object and by ID of the sentence. The return value contains the sentence text itself. The returned slice contains sentences sorted by their IDs started from the given sentence ID in ascending sort.

func FindSingleWords 

func FindSingleWords(textRank *TextRank) []rank.SingleWord

FindSingleWords function retrieves a slice of SingleWord structures by TextRank object. The return value contains the sorted words with IDs, words, weights and quantities by weight from 1 to 0. Weight is calculated from quantities of word.

func NewChainAlgorithm 

func NewChainAlgorithm() *rank.AlgorithmChain

NewChainAlgorithm function retrieves an Algorithm object. It defines how should work the text ranking algorithm, the weighting. This is an alternative way to ranking words by weighting the number of the words. Because Algorithm is an interface it's possible to modify the ranking algorithm by inject different implementation. This is the 4th step to use TextRank.

func NewDefaultAlgorithm 

func NewDefaultAlgorithm() *rank.AlgorithmDefault

NewDefaultAlgorithm function retrieves an Algorithm object. It defines how should work the text ranking algorithm, the weighting. This is the general text rank by weighting the connection between the words to find the strongest phrases. Because Algorithm is an interface it's possible to modify the ranking algorithm by inject different implementation. This is the 4th step to use TextRank.

func NewDefaultLanguage 

func NewDefaultLanguage() *convert.LanguageDefault

NewDefaultLanguage function retrieves a default Language object. It defines what words are real and what words are just Stop Words or useless Junk Words. It uses the default English Stop Words, but it's possible to set different Stop Words in English or any other languages. Because Language is an interface it's possible to modify the ranking by inject different Language implementation. This is the 3rd step to use TextRank.

func NewDefaultRule 

func NewDefaultRule() *parse.RuleDefault

NewDefaultRule function retrieves a default Rule object what works in the most cases in English or similar Latin languages like French or Spanish. The Rule defines raw text how should be split to sentences and words. Because Rule is an interface it's possible modify the ranking by inject different Rule implementation. This is the 2nd step to use TextRank.

Types

type TextRank 

type TextRank struct {
	// contains filtered or unexported fields
}

TextRank structure contains the Rank data object. This structure is a wrapper around the whole text ranking functionality.

func NewTextRank 

func NewTextRank() *TextRank

NewTextRank constructor retrieves a TextRank pointer. This is the 1th step to use TextRank.

func (*TextRank) GetRankData 

func (textRank *TextRank) GetRankData() *rank.Rank

GetRankData method retrieves the Rank data to that case if the developer want access to the whole graph and sentences, words, weights and all of the data to analyze it or just implement a new search logic or finder method.

func (*TextRank) Populate 

func (textRank *TextRank) Populate(
	text string,
	lang convert.Language,
	rule parse.Rule,
)

Populate method adds a raw text to the text-ranking graph. It parses, tokenize the raw text and prepares it to weighting and scoring. It's possible to append a new raw text to an existing one even if the previously text is already ranked. This is 5th step to use TextRank.

text string must be a plain text from TXT or PDF or any document, it can contain new lines, break lines or any unnecessary text parts, but it should not contain HTML tags or codes.

lang Language object can be loaded from NewDefaultLanguage function.

rule Rule object can be loaded from NewDefaultRule function.

func (*TextRank) Ranking 

func (textRank *TextRank) Ranking(algorithm rank.Algorithm)

Ranking method counts the words and connections between the words, then it weights the numbers then normalize them in type float32 between 0.00 and 1.00. This is the 6th step to use TextRank.

algorithm Algorithm is the object of the weighting and scoring methods.

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