## Textrank

TextRank – is a graph-based ranking model for text processing which can be used in order to find the most relevant sentences in text and also to find keywords. The algorithm is explained in detail in the paper at https://web.eecs.umich.edu/~mihalcea/papers/mihalcea.emnlp04.pdf

### Identify relevant sentences

In order to find the most relevant sentences in text, a graph is constructed where the vertices of the graph represent each sentence in a document and the edges between sentences are based on content overlap, namely by calculating the number of words that 2 sentences have in common.

Based on this network of sentences, the sentences are fed into the Pagerank algorithm which identifies the most important sentences. When we want to extract a summary of the text, we can now take only the most important sentences.

### Identify relevant keywords

In order to find relevant keywords, the textrank algorithm constructs a word network. This network is constructed by looking which words follow one another. A link is set up between two words if they follow one another, the link gets a higher weight if these 2 words occur more frequenctly next to each other in the text.

On top of the resulting network the Pagerank algorithm is applied to get the importance of each word. The top 1/3 of all these words are kept and are considered relevant. After this, a keywords table is constructed by combining the relevant words together if they appear following one another in the text.

### Example text

To show how you can apply textrank, the package includes a job description, which is printed below. We want to extract the most important sentences in the job description as well as keywords.

library(textrank)
data(joboffer)
cat(unique(joboffer$sentence), sep = "\n") Statistical expert / data scientist / analytical developer BNOSAC (Belgium Network of Open Source Analytical Consultants), is a Belgium consultancy company specialized in data analysis and statistical consultancy using open source tools. In order to increase and enhance the services provided to our clients, we are on the lookout for an all-round statistical expert, data scientist and analytical developer. Function: Your main task will be the execution of a diverse range of consultancy services in the field of statistics and data science. You will be involved in a small team where you handle the consultancy services from the start of the project until the end. This covers: Joint meeting with clients on the topic of the analysis. Acquaintance with the data. Analysis of the techniques that are required to execute the study. Mostly standard statistical and biostatistical modelling, predictive analytics & machine learning techniques. Perform statistical design, modeling and analysis, together with more seniors. Building the report on the data analysis. Automating and R/Python package development. Integration of the models into the existing architecture. Giving advise to the client on the research questions, design or integration. Next to that, you will help in building data products and help sell them. These cover text mining, integration of predictive analytics in existing tools and the creation of specific data analysis tools and web services. You also might be involved in providing data science related courses for clients. Profile: You have a master degree in the domain of Statistics, Biostatistics, Mathematics, Commercial or Industrial Engineering, Economics or similar. You have a strong interest in statistics and data analysis. You have good communication skills, are fluent in English and know either Dutch or French. You soak up new knowledge and either just make things work or have the attitude of 'I can do this'. Besides this, you have attention to detail and adapt to changes quickly. You have programming experience in R or you really want to switch to using R. You have a sound knowledge of another data analysis language (Python, SQL, javascript) and you don't care in which relational database, Excel, bigdata or noSQL store your data is located. Interested in robotics is a plus. Offer: A half or full-time employment depending on your personal situation. The ability to get involved in a whole range of sectors and topics and the flexibility to shape your own future. The usage of a diverse range of statistical & data science techniques. Support in getting up to speed quickly in the usage of R. An environment in which you can develop your talent and make your own proposals the standard way to go. Liberty in managing your open source projects during working hours. Contact: To apply or in order to get more information about the job content, please contact us at: http://bnosac.be/index.php/contact/get-in-touch The textrank algorithm (keyword extraction / sentence ranking) requires as input the identification of words which are relevant in your domain. This is normally done by doing Parts of Speech tagging which can be done using a broad range of R packages. In the example on the joboffer, we did Parts of Speech tagging using the udpipe R package (https://github.com/bnosac/udpipe) so that we have a sentence identifier and a parts of speech tag for each word in the job offer. Which is exactly what we need for extracting keywords as well as for sentence ranking as the Parts of Speech tag allows us to easily remove irrelevant words. head(joboffer[, c("sentence_id", "lemma", "upos")], 10)  sentence_id lemma upos 1 1 Statistical ADJ 2 1 expert NOUN 3 1 / PUNCT 4 1 data NOUN 5 1 scientist NOUN 6 1 / PUNCT 7 1 analytical ADJ 8 1 developer NOUN 9 2 BNOSAC PROPN 10 2 ( PUNCT You can get that joboffer data.frame as follows. job_rawtxt <- readLines(system.file(package = "textrank", "extdata", "joboffer.txt")) job_rawtxt <- paste(job_rawtxt, collapse = "\n") library(udpipe) tagger <- udpipe_download_model("english") tagger <- udpipe_load_model(tagger$file_model)
joboffer <- udpipe_annotate(tagger, job_rawtxt)
joboffer <- as.data.frame(joboffer)

## Textrank for keyword extraction

For extracting keywords in the job description, we are providing it a vector of words and a vector of logicals indicating for each word if it is relevant. In the below case we consider only nouns, verbs and adjectives as relevant.

keyw <- textrank_keywords(joboffer$lemma, relevant = joboffer$upos %in% c("NOUN", "VERB", "ADJ"))
subset(keyw$keywords, ngram > 1 & freq > 1)  keyword ngram freq 4 data-analysis 2 4 9 data-science 2 3 14 consultancy-service 2 2 ## Textrank for sentence ranking The algorithm basically computes weights between sentences by looking which words are overlapping. You probably do not want to look for overlap in words like ‘the’, ‘and’, ‘or’, … That is why, most of the time you probably will have already executed some Parts of Speech tagging in order to identify nouns, verbs, adjectives, … or you might have set up your own dictionary of words which you want to consider to find overlap between sentences. head(joboffer[, c("sentence_id", "lemma", "upos")], 10)  sentence_id lemma upos 1 1 Statistical ADJ 2 1 expert NOUN 3 1 / PUNCT 4 1 data NOUN 5 1 scientist NOUN 6 1 / PUNCT 7 1 analytical ADJ 8 1 developer NOUN 9 2 BNOSAC PROPN 10 2 ( PUNCT ### Define sentences and terminology In order to apply textrank for sentence ranking, we need to feed the function textrank_sentences 2 inputs: - a data.frame with sentences and - a data.frame with words which are part of each sentence. In the following example we start by creating a sentence identifier which is a combination of a document/paragraph and sentence identifier and we take only nouns and adjectives for finding overlap between sentences. library(udpipe) joboffer$textrank_id <- unique_identifier(joboffer, c("doc_id", "paragraph_id", "sentence_id"))
sentences <- unique(joboffer[, c("textrank_id", "sentence")])
terminology <- subset(joboffer, upos %in% c("NOUN", "ADJ"))
terminology <- terminology[, c("textrank_id", "lemma")]
head(terminology)
  textrank_id       lemma
1           1 Statistical
2           1      expert
4           1        data
5           1   scientist
7           1  analytical
8           1   developer

### Applying textrank_sentences

When applying textrank_sentences it looks for word (nouns/adjectives in this case) which are the same in sentences and next applies Google Pagerank on the sentence network. The result is an object of class textrank_sentences which contains the sentences, the links between the sentences and the result of Google’s Pagerank.

## Textrank for finding the most relevant sentences
tr <- textrank_sentences(data = sentences, terminology = terminology)
names(tr)
[1] "sentences"      "sentences_dist" "pagerank"      
plot(sort(tr$pagerank$vector, decreasing = TRUE),
type = "b", ylab = "Pagerank", main = "Textrank")

Using the summary function, we can extract the top n most relevant sentences. By default it gives the sentences in order of Pagerank importance but you can also get the n most important sentences and keep the sentence order as provided in the original sentences data.frame.

s <- summary(tr, n = 4)
s <- summary(tr, n = 4, keep.sentence.order = TRUE)
cat(s, sep = "\n")
BNOSAC (Belgium Network of Open Source Analytical Consultants), is a Belgium consultancy company specialized in data analysis and statistical consultancy using open source tools.
Building the report on the data analysis.
You have a strong interest in statistics and data analysis.
The usage of a diverse range of statistical & data science techniques.

Mark that the textrank_sentences function has a textrank_dist argument, which allows you to provide any distance type of calculation you prefer. This can e.g. be used to change the distance calculation to something based on word vectors if you like, based on Levenshtein distances, functions from the textreuse package, based on stemming or any complex calculation you prefer.

### Minhash

In the above example, there were 37 sentences. Which gives 666 combinations of sentences to calculate word overlap. If you have a large number of sentences, this becomes computationally unfeasible.

That is why you can provide in the argument textrank_candidates a data.frame with sentence combinations for which you want to compute the Jaccard distance. This can be used for example to reduce the number of sentence combinations by applying the Minhash algorithm as shown below.

The result is a you saving computation time. For good settings on n and bands which should be set in conjunction with the textrank_dist function, have a look at the vignette of the textreuse package.

## Limit the number of candidates with the minhash algorithm
library(textreuse)
minhash <- minhash_generator(n = 1000, seed = 123456789)
candidates <- textrank_candidates_lsh(x = terminology$lemma, sentence_id = terminology$textrank_id,
minhashFUN = minhash,
bands = 500)
dim(candidates)
[1] 85  2
head(candidates)
  textrank_id_1 textrank_id_2
1             1             3
2            13            22
3            10            32
4            12            32
5            11             2
6            12             2
tr <- textrank_sentences(data = sentences, terminology = terminology, textrank_candidates = candidates)
s <- summary(tr, n = 4, keep.sentence.order = TRUE)
cat(s, sep = "\n")
BNOSAC (Belgium Network of Open Source Analytical Consultants), is a Belgium consultancy company specialized in data analysis and statistical consultancy using open source tools.
Building the report on the data analysis.
You have a strong interest in statistics and data analysis.
The usage of a diverse range of statistical & data science techniques.

## Support in text mining

Need support in text mining. Contact BNOSAC: http://www.bnosac.be