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Text Summarization

This section will demonstrate how to use TextAnalysis.jl (Julia's leading NLP library) for QuranTree.jl. In particular, in summarizing the Qur'an, specifically Chapter 18 (The Cave) which most Muslims are familiar with (this is the chapter recommended to be read every Friday). The algorithm used for summarization is called TextRank, an application of PageRank algorithm to text datasets.

julia> using QuranTree
julia> using TextAnalysis
julia> using Yunir
julia> crps, tnzl = QuranData() |> load;
julia> crpsdata = table(crps)Quranic Arabic Corpus (morphology)
(C) 2011 Kais Dukes

128219×7 DataFrame
    Row │ chapter  verse  word   part   form        tag     features           ⋯
        │ Int64    Int64  Int64  Int64  String      String  String             ⋯
────────┼───────────────────────────────────────────────────────────────────────
      1 │       1      1      1      1  bi          P       PREFIX|bi+         ⋯
      2 │       1      1      1      2  somi        N       STEM|POS:N|LEM:{so
      3 │       1      1      2      1  {ll~ahi     PN      STEM|POS:PN|LEM:{l
      4 │       1      1      3      1  {l          DET     PREFIX|Al+
      5 │       1      1      3      2  r~aHoma`ni  ADJ     STEM|POS:ADJ|LEM:r ⋯
      6 │       1      1      4      1  {l          DET     PREFIX|Al+
      7 │       1      1      4      2  r~aHiymi    ADJ     STEM|POS:ADJ|LEM:r
      8 │       1      2      1      1  {lo         DET     PREFIX|Al+
   ⋮    │    ⋮       ⋮      ⋮      ⋮        ⋮         ⋮                     ⋮  ⋱
 128213 │     114      5      5      2  n~aAsi      N       STEM|POS:N|LEM:n~a ⋯
 128214 │     114      6      1      1  mina        P       STEM|POS:P|LEM:min
 128215 │     114      6      2      1  {lo         DET     PREFIX|Al+
 128216 │     114      6      2      2  jin~api     N       STEM|POS:N|LEM:jin
 128217 │     114      6      3      1  wa          CONJ    PREFIX|w:CONJ+     ⋯
 128218 │     114      6      3      2  {l          DET     PREFIX|Al+
 128219 │     114      6      3      3  n~aAsi      N       STEM|POS:N|LEM:n~a
                                                1 column and 128204 rows omitted
Note

You need to install Yunir.jl to successfully run the code. 

using Pkg
Pkg.add("Yunir")
Pkg.add("TextAnalysis")

Data Preprocessing

The first data processing will be the removal of all Disconnected Letters (like الٓمٓ ,الٓمٓصٓ, among others), Prepositions, Particles, Conjunctions, Pronouns, and Adverbs. This is done as follows:

julia> function preprocess(s::String)
           feat = parse(QuranFeatures, s)
           disletters = isfeat(feat, AbstractDisLetters)
           prepositions = isfeat(feat, AbstractPreposition)
           particles = isfeat(feat, AbstractParticle)
           conjunctions = isfeat(feat, AbstractConjunction)
           pronouns = isfeat(feat, AbstractPronoun)
           adverbs = isfeat(feat, AbstractAdverb)
       
           return !disletters && !prepositions && !particles && !conjunctions && !pronouns && !adverbs
       endpreprocess (generic function with 1 method)
julia> crpstbl = filter(t -> preprocess(t.features), crpsdata[18].data)827×7 DataFrame
 Row │ chapter  verse  word   part   form       tag     features               ⋯
     │ Int64    Int64  Int64  Int64  String     String  String                 ⋯
─────┼──────────────────────────────────────────────────────────────────────────
   1 │      18      1      1      2  Hamodu     N       STEM|POS:N|LEM:Hamod|R ⋯
   2 │      18      1      2      2  l~ahi      PN      STEM|POS:PN|LEM:{ll~ah
   3 │      18      1      4      1  >anzala    V       STEM|POS:V|PERF|(IV)|L
   4 │      18      1      6      1  Eabodi     N       STEM|POS:N|LEM:Eabod|R
   5 │      18      1      9      1  yajoEal    V       STEM|POS:V|IMPF|LEM:ja ⋯
   6 │      18      1     11      1  EiwajaA    N       STEM|POS:N|LEM:Eiwaj|R
   7 │      18      2      2      1  l~i        PRP     PREFIX|l:PRP+
   8 │      18      2      2      2  yun*ira    V       STEM|POS:V|IMPF|(IV)|L
  ⋮  │    ⋮       ⋮      ⋮      ⋮        ⋮        ⋮                     ⋮      ⋱
 821 │      18    110     14      1  yarojuwA@  V       STEM|POS:V|IMPF|LEM:ya ⋯
 822 │      18    110     16      1  rab~i      N       STEM|POS:N|LEM:rab~|RO
 823 │      18    110     17      2  lo         IMPV    PREFIX|l:IMPV+
 824 │      18    110     17      3  yaEomalo   V       STEM|POS:V|IMPF|LEM:Ea
 825 │      18    110     21      1  yu$oriko   V       STEM|POS:V|IMPF|(IV)|L ⋯
 826 │      18    110     22      2  EibaAdapi  N       STEM|POS:N|LEM:EibaAda
 827 │      18    110     23      1  rab~i      N       STEM|POS:N|LEM:rab~|RO
                                                   1 column and 812 rows omitted

Next, we create a copy of the above data (so we have the original state), and use the copy to do further data processing.

julia> crpsnew = deepcopy(crpstbl)827×7 DataFrame
 Row │ chapter  verse  word   part   form       tag     features               ⋯
     │ Int64    Int64  Int64  Int64  String     String  String                 ⋯
─────┼──────────────────────────────────────────────────────────────────────────
   1 │      18      1      1      2  Hamodu     N       STEM|POS:N|LEM:Hamod|R ⋯
   2 │      18      1      2      2  l~ahi      PN      STEM|POS:PN|LEM:{ll~ah
   3 │      18      1      4      1  >anzala    V       STEM|POS:V|PERF|(IV)|L
   4 │      18      1      6      1  Eabodi     N       STEM|POS:N|LEM:Eabod|R
   5 │      18      1      9      1  yajoEal    V       STEM|POS:V|IMPF|LEM:ja ⋯
   6 │      18      1     11      1  EiwajaA    N       STEM|POS:N|LEM:Eiwaj|R
   7 │      18      2      2      1  l~i        PRP     PREFIX|l:PRP+
   8 │      18      2      2      2  yun*ira    V       STEM|POS:V|IMPF|(IV)|L
  ⋮  │    ⋮       ⋮      ⋮      ⋮        ⋮        ⋮                     ⋮      ⋱
 821 │      18    110     14      1  yarojuwA@  V       STEM|POS:V|IMPF|LEM:ya ⋯
 822 │      18    110     16      1  rab~i      N       STEM|POS:N|LEM:rab~|RO
 823 │      18    110     17      2  lo         IMPV    PREFIX|l:IMPV+
 824 │      18    110     17      3  yaEomalo   V       STEM|POS:V|IMPF|LEM:Ea
 825 │      18    110     21      1  yu$oriko   V       STEM|POS:V|IMPF|(IV)|L ⋯
 826 │      18    110     22      2  EibaAdapi  N       STEM|POS:N|LEM:EibaAda
 827 │      18    110     23      1  rab~i      N       STEM|POS:N|LEM:rab~|RO
                                                   1 column and 812 rows omitted
julia> feats = crpsnew[!, :features]827-element Vector{String}:
 "STEM|POS:N|LEM:Hamod|ROOT:Hmd|M|NOM"
 "STEM|POS:PN|LEM:{ll~ah|ROOT:Alh|GEN"
 "STEM|POS:V|PERF|(IV)|LEM:>anzala|ROOT:nzl|3MS"
 "STEM|POS:N|LEM:Eabod|ROOT:Ebd|M|GEN"
 "STEM|POS:V|IMPF|LEM:jaEala|ROOT:jEl|3MS|MOOD:JUS"
 "STEM|POS:N|LEM:Eiwaj|ROOT:Ewj|M|NOM"
 "PREFIX|l:PRP+"
 "STEM|POS:V|IMPF|(IV)|LEM:>an*ara|ROOT:n*r|3MS|MOOD:SUBJ"
 "STEM|POS:N|LEM:l~adun|ROOT:ldn|GEN"
 "STEM|POS:V|IMPF|(II)|LEM:bu\$~ira|ROOT:b\$r|3MS|MOOD:SUBJ"
 ⋮
 "STEM|POS:ADJ|LEM:wa`Hid|ROOT:wHd|MS|INDEF|NOM"
 "STEM|POS:V|PERF|LEM:kaAna|ROOT:kwn|SP:kaAn|3MS"
 "STEM|POS:V|IMPF|LEM:yarojuwA@|ROOT:rjw|3MS"
 "STEM|POS:N|LEM:rab~|ROOT:rbb|M|GEN"
 "PREFIX|l:IMPV+"
 "STEM|POS:V|IMPF|LEM:Eamila|ROOT:Eml|3MS|MOOD:JUS"
 "STEM|POS:V|IMPF|(IV)|LEM:>a\$oraka|ROOT:\$rk|3MS|MOOD:JUS"
 "STEM|POS:N|LEM:EibaAdat|ROOT:Ebd|F|GEN"
 "STEM|POS:N|LEM:rab~|ROOT:rbb|M|GEN"
julia> feats = parse.(QuranFeatures, feats)827-element Vector{AbstractQuranFeature}:
 Stem(:N, N, AbstractQuranFeature[Lemma("Hamod"), Root("Hmd"), M, NOM])
 Stem(:PN, PN, AbstractQuranFeature[Lemma("{ll~ah"), Root("Alh"), GEN])
 Stem(:V, V, AbstractQuranFeature[Lemma(">anzala"), Root("nzl"), PERF, IV, 3, M, S, IND, ACT])
 Stem(:N, N, AbstractQuranFeature[Lemma("Eabod"), Root("Ebd"), M, GEN])
 Stem(:V, V, AbstractQuranFeature[Lemma("jaEala"), Root("jEl"), JUS, IMPF, 3, M, S, ACT, I])
 Stem(:N, N, AbstractQuranFeature[Lemma("Eiwaj"), Root("Ewj"), M, NOM])
 Prefix(Symbol("l:PRP+"), PRP)
 Stem(:V, V, AbstractQuranFeature[Lemma(">an*ara"), Root("n*r"), SUBJ, IMPF, IV, 3, M, S, ACT])
 Stem(:N, N, AbstractQuranFeature[Lemma("l~adun"), Root("ldn"), GEN])
 Stem(:V, V, AbstractQuranFeature[Lemma("bu\$~ira"), Root("b\$r"), SUBJ, IMPF, II, 3, M, S, ACT])
 ⋮
 Stem(:ADJ, ADJ, AbstractQuranFeature[Lemma("wa`Hid"), Root("wHd"), M, S, INDEF, NOM])
 Stem(:V, V, AbstractQuranFeature[Lemma("kaAna"), Root("kwn"), Special("kaAn"), PERF, 3, M, S, IND, ACT, I])
 Stem(:V, V, AbstractQuranFeature[Lemma("yarojuwA@"), Root("rjw"), IMPF, 3, M, S, IND, ACT, I])
 Stem(:N, N, AbstractQuranFeature[Lemma("rab~"), Root("rbb"), M, GEN])
 Prefix(Symbol("l:IMPV+"), IMPV)
 Stem(:V, V, AbstractQuranFeature[Lemma("Eamila"), Root("Eml"), JUS, IMPF, 3, M, S, ACT, I])
 Stem(:V, V, AbstractQuranFeature[Lemma(">a\$oraka"), Root("\$rk"), JUS, IMPF, IV, 3, M, S, ACT])
 Stem(:N, N, AbstractQuranFeature[Lemma("EibaAdat"), Root("Ebd"), F, GEN])
 Stem(:N, N, AbstractQuranFeature[Lemma("rab~"), Root("rbb"), M, GEN])

Lemmatization

Using the above parsed features, we then convert the form of the tokens into its lemma. This is useful for addressing variations due to inflection.

julia> lemmas = lemma.(feats)827-element Vector{Union{Missing, String}}:
 "Hamod"
 "{ll~ah"
 ">anzala"
 "Eabod"
 "jaEala"
 "Eiwaj"
 missing
 ">an*ara"
 "l~adun"
 "bu\$~ira"
 ⋮
 "wa`Hid"
 "kaAna"
 "yarojuwA@"
 "rab~"
 missing
 "Eamila"
 ">a\$oraka"
 "EibaAdat"
 "rab~"
julia> forms1 = crpsnew[!, :form]827-element Vector{String}:
 "Hamodu"
 "l~ahi"
 ">anzala"
 "Eabodi"
 "yajoEal"
 "EiwajaA"
 "l~i"
 "yun*ira"
 "l~aduno"
 "yuba\$~ira"
 ⋮
 "wa`HidN"
 "kaAna"
 "yarojuwA@"
 "rab~i"
 "lo"
 "yaEomalo"
 "yu\$oriko"
 "EibaAdapi"
 "rab~i"
julia> forms1[.!ismissing.(lemmas)] = lemmas[.!ismissing.(lemmas)]795-element Vector{Union{Missing, String}}:
 "Hamod"
 "{ll~ah"
 ">anzala"
 "Eabod"
 "jaEala"
 "Eiwaj"
 ">an*ara"
 "l~adun"
 "bu\$~ira"
 "Eamila"
 ⋮
 "<ila`h"
 "wa`Hid"
 "kaAna"
 "yarojuwA@"
 "rab~"
 "Eamila"
 ">a\$oraka"
 "EibaAdat"
 "rab~"
Tips

We can also use the Root features instead, which is done by simply replacing lemma.(feats) with root.(feats).

We now put back the new form to the corpus:

julia> crpsnew[!, :form] = forms1827-element Vector{String}:
 "Hamod"
 "{ll~ah"
 ">anzala"
 "Eabod"
 "jaEala"
 "Eiwaj"
 "l~i"
 ">an*ara"
 "l~adun"
 "bu\$~ira"
 ⋮
 "wa`Hid"
 "kaAna"
 "yarojuwA@"
 "rab~"
 "lo"
 "Eamila"
 ">a\$oraka"
 "EibaAdat"
 "rab~"
julia> crpsnew = CorpusData(crpsnew)Quranic Arabic Corpus (morphology)
(C) 2011 Kais Dukes

827×7 DataFrame
 Row │ chapter  verse  word   part   form       tag     features               ⋯
     │ Int64    Int64  Int64  Int64  String     String  String                 ⋯
─────┼──────────────────────────────────────────────────────────────────────────
   1 │      18      1      1      2  Hamod      N       STEM|POS:N|LEM:Hamod|R ⋯
   2 │      18      1      2      2  {ll~ah     PN      STEM|POS:PN|LEM:{ll~ah
   3 │      18      1      4      1  >anzala    V       STEM|POS:V|PERF|(IV)|L
   4 │      18      1      6      1  Eabod      N       STEM|POS:N|LEM:Eabod|R
   5 │      18      1      9      1  jaEala     V       STEM|POS:V|IMPF|LEM:ja ⋯
   6 │      18      1     11      1  Eiwaj      N       STEM|POS:N|LEM:Eiwaj|R
   7 │      18      2      2      1  l~i        PRP     PREFIX|l:PRP+
   8 │      18      2      2      2  >an*ara    V       STEM|POS:V|IMPF|(IV)|L
  ⋮  │    ⋮       ⋮      ⋮      ⋮        ⋮        ⋮                     ⋮      ⋱
 821 │      18    110     14      1  yarojuwA@  V       STEM|POS:V|IMPF|LEM:ya ⋯
 822 │      18    110     16      1  rab~       N       STEM|POS:N|LEM:rab~|RO
 823 │      18    110     17      2  lo         IMPV    PREFIX|l:IMPV+
 824 │      18    110     17      3  Eamila     V       STEM|POS:V|IMPF|LEM:Ea
 825 │      18    110     21      1  >a$oraka   V       STEM|POS:V|IMPF|(IV)|L ⋯
 826 │      18    110     22      2  EibaAdat   N       STEM|POS:N|LEM:EibaAda
 827 │      18    110     23      1  rab~       N       STEM|POS:N|LEM:rab~|RO
                                                   1 column and 812 rows omitted

Tokenization

We want to summarize the Qur'an at the verse level. Thus, the token would be the verses of the corpus. From these verses, we further clean it by dediacritization and normalization of the characters:

julia> lem_vrs = verses(crpsnew)109-element Vector{String}:
 "Hamod {ll~ah >anzala Eabod jaEala Eiwaj"
 "l~i>an*ara l~adun bu\$~ira Eamila"
 ">an*ara qaAla {t~axa*a {ll~ah"
 "Eilom A^baA' kabura xaraja >afowa`h qaAla"
 "ba`xiE >avar 'aAmana Hadiyv"
 "jaEala >aroD libalawo >aHosan"
 "lajaAEil"
 "Hasiba kahof r~aqiym kaAna 'aAyap"
 ">awaY fitoyap kahof qaAla A^taY l~adun yuhay~i}o >amor"
 "Daraba >u*unN kahof"
 ⋮
 "Hasiba kafara {t~axa*a Eabod duwn >aEotadato jahan~am ka`firuwn"
 "qaAla nab~a>a >axosariyn"
 "Dal~a saEoy Hayaw`p d~unoyaA Hasiba >aHosana"
 "kafara 'aAyap rab~ liqaA^' HabiTa Eamal >aqaAma qiya`map"
 "jazaA^' jahan~am kafara {t~axa*a 'aAyap rasuwl"
 "'aAmana Eamila S~a`liHa`t kaAna jan~ap firodawos"
 "bagaY`"
 "qaAla kaAna baHor kalima`t rab~ lanafida baHor nafida kalima`t rab~ jaA^'a mivol"
 "qaAla ba\$ar mivol >awoHaY`^ <ila`h <ila`h wa`Hid kaAna yarojuwA@ rab~ loEamila >a\$oraka EibaAdat rab~"
julia> vrs = normalize.(dediac.(lem_vrs))109-element Vector{String}:
 "Hmd Allh Anzl Ebd jEl Ewj"
 "lAn*r ldn b\$r Eml"
 "An*r qAl Atx* Allh"
 "Elm AbA' kbr xrj Afwh qAl"
 "bxE Avr 'Amn Hdyv"
 "jEl ArD lblw AHsn"
 "ljAEl"
 "Hsb khf rqym kAn 'Ayh"
 "Awy ftyh khf qAl Aty ldn yhyy Amr"
 "Drb A*n khf"
 ⋮
 "Hsb kfr Atx* Ebd dwn AEtdt jhnm kfrwn"
 "qAl nbA Axsryn"
 "Dl sEy Hywh dnyA Hsb AHsn"
 "kfr 'Ayh rb lqA' HbT Eml AqAm qymh"
 "jzA' jhnm kfr Atx* 'Ayh rswl"
 "'Amn Eml SlHt kAn jnh frdws"
 "bgy"
 "qAl kAn bHr klmt rb lnfd bHr nfd klmt rb jA' mvl"
 "qAl b\$r mvl AwHy Alh Alh wHd kAn yrjwA@ rb lEml A\$rk EbAdt rb"

Creating a TextAnalysis Corpus

To make use of the TextAnalysis.jl's APIs, we need to encode the processed Quranic Corpus to TextAnalysis.jl's Corpus. In this case, we will create a StringDocument of the verses.

julia> crps1 = Corpus(StringDocument.(vrs))A Corpus with 109 documents:
 * 109 StringDocument's
 * 0 FileDocument's
 * 0 TokenDocument's
 * 0 NGramDocument's

Corpus's lexicon contains 0 tokens
Corpus's index contains 0 tokens

We then update the lexicon and inverse index for efficient indexing of the corpus.

julia> update_lexicon!(crps1)
julia> update_inverse_index!(crps1)

Next, we create a Document Term Matrix, which will have rows of verses and columns of words describing the verses.

julia> m1 = DocumentTermMatrix(crps1)A 109 X 360 DocumentTermMatrix

TF-IDF

Finally, we compute the corresponding TF-IDF, which will serve as the feature matrix.

julia> tfidf = tf_idf(m1)109×360 SparseArrays.SparseMatrixCSC{Float64, Int64} with 836 stored entries:
⡀⣇⡀⢖⢐⣹⢑⠀⣵⡢⡙⠈⢺⠀⡈⢔⣀⡠⣀⠃⢲⣀⠵⡇⡀⡀⢠⠀⣧⣀⣡⡂⢀⠀⣀⣀⠐⠀⣄⠠
⠮⠷⢯⠔⠡⡫⠰⠦⡔⠫⠭⠦⠮⡤⣅⠉⢜⠠⠹⢤⡔⣿⠹⢅⠕⠍⠈⣄⡏⠁⢹⠨⠑⡤⣋⡕⢋⢥⠄⠁
⢑⡟⠐⢇⠄⢳⣺⢁⣅⣒⠠⠧⠐⢒⢤⡐⡾⠔⢠⡬⣖⢠⠀⠀⡳⢠⣄⡲⡇⡂⡽⠂⠸⡔⠀⢦⠶⠂⠄⢰
⠴⡇⠡⠈⠈⢅⠂⡆⠖⠁⠶⠂⠀⠄⠊⠛⠋⠘⢉⠃⠂⢛⠁⢒⠈⠁⠨⢀⡏⢁⢋⠠⢂⠀⠀⠹⢀⠨⢁⡁
⡝⠇⢈⠐⠃⠸⠙⠋⡏⠠⠨⠠⠠⠄⡠⢹⢑⠁⠆⡂⡓⠂⠁⠁⠀⠚⠁⠈⠇⢎⠰⠘⡈⠘⠂⢪⠂⠑⡘⠂
⠀⣏⠂⠄⠈⡠⠌⠃⢇⠤⡢⠲⠱⠼⢑⡀⠔⠔⢈⠱⡗⡂⢀⢠⠀⠉⡀⢸⡅⠩⢩⠄⠅⠀⠐⢈⠀⠁⢐⠀

Summarizing the Qur'an

Using the TF-IDF, we compute the product of it with its transpose to come up with a square matrix, where the elements describes the linkage between the verses, or the similarity between the verses.

julia> sim_mat = tfidf * tfidf'109×109 SparseArrays.SparseMatrixCSC{Float64, Int64} with 5199 stored entries:
⠻⣦⢡⠖⣴⢻⡆⢷⡇⡗⡖⠱⠆⢿⣴⢻⢰⡷⣼⣶⡗⠗⢶⠷⢾⡶⢶⣶⠆⠰⡴⢶⠁⠸⠿⡇⡞⢶⣶⡶
⢡⠖⢱⣶⠾⣶⡆⡴⣶⣷⢇⡱⠄⢴⠶⢚⢐⣷⢻⢲⣃⢅⢴⠤⠴⠦⡴⢶⡖⢰⢧⡴⠁⠨⠭⠇⣒⠜⡒⠶
⣴⣛⢺⣧⣿⣿⡇⣿⣧⡷⡧⠰⠺⣿⣿⣾⢟⡿⣛⣻⣿⠢⢾⡔⢺⡓⢞⣻⠆⠿⣷⢿⡆⢰⠷⡇⡷⢺⣟⡿
⢬⣍⢈⡭⣭⣭⡕⣭⡍⡭⡭⢨⣭⣭⣭⢬⣍⣭⣩⣩⣭⡭⣭⣨⣭⣭⣭⣭⡅⣭⣩⣭⠀⢨⣽⡅⡭⣭⣍⣭
⢭⠭⢼⣿⢭⡿⡇⡭⡵⢏⢍⢨⠭⠭⠭⣭⡭⢭⣭⣭⣭⢍⢭⢬⡭⡭⡭⢭⣅⣭⢭⣭⡄⡨⠭⡅⣭⢭⣭⣭
⢜⡉⢍⡱⢉⡋⡃⣋⡃⣑⣱⣾⡉⣉⣏⣸⠉⣝⣩⣙⠟⡕⣙⣈⣉⣉⣋⣙⣃⢙⣙⣋⠌⢨⣭⡅⡛⡉⢻⣉
⣬⣅⢀⣅⣾⣦⡇⣿⡇⡇⡇⢨⡿⣯⣾⣤⢆⣿⣹⣢⡶⡅⣯⢨⣭⣯⣽⣭⡅⢾⣨⣯⠀⢨⣯⡇⡠⣵⣄⣿
⣴⣛⣸⢃⣻⣿⡃⣟⡇⣧⣋⣹⠚⣿⣛⣼⣛⣻⣟⣟⣻⢂⢻⠑⢺⡓⣚⣛⣦⢻⣓⣛⠔⢰⡛⡇⣧⢚⣻⣿
⢴⡶⢴⣴⣿⡵⡇⣽⡇⣏⣇⢤⣬⣵⣿⣸⡵⣯⣶⣶⣯⣄⣴⢤⣴⣦⣴⣶⣄⣭⢦⣵⡄⢠⣥⡇⣆⣼⣾⣯
⢲⣿⢻⣒⣿⣸⡇⣺⡇⣿⣇⢺⠳⣺⣿⢽⢸⣿⣿⣿⣗⢂⣾⡒⢺⡗⣺⣿⡯⢸⣗⣺⠂⠘⢒⡇⣇⢺⣿⡿
⢽⠍⠍⢜⠻⡛⡇⡿⡇⢟⢟⠥⠜⠯⠻⢚⠋⢿⠹⢙⣟⣽⢼⠢⠼⠥⡯⣽⡇⢻⢹⡯⡇⢸⠯⠇⣛⠜⡋⡿
⢼⡗⠐⡗⢚⠷⡃⣻⡃⣗⡓⢸⡋⣛⢟⠒⠐⣟⢺⠻⠲⡓⣟⣽⣻⣛⣛⣿⡓⢘⢿⣛⡄⣸⣻⡃⠲⡓⠒⣛
⢺⡷⠰⡇⢾⠲⡇⣿⡇⡯⡇⢸⡧⣿⢾⠲⠰⣿⢾⠶⠖⡇⣿⢺⣿⣿⣿⣿⡇⢸⢾⣿⠎⢼⣿⡇⡇⡷⠶⣿
⢸⣷⢰⣏⣾⣱⡇⣿⡇⣏⣏⢸⡗⣿⣾⢸⢰⣿⣾⣾⣏⣯⣿⣼⣿⣿⣿⣿⡏⣸⣾⣿⡄⢸⣿⡇⣏⣷⣷⣿
⢈⡁⢘⣉⣬⡅⡅⣭⡅⣽⣍⢘⣡⣍⣬⣛⡄⣽⣋⣋⣭⣉⣙⢈⣉⣉⣋⣩⣟⣽⢈⣍⡀⢈⣍⡇⣋⣩⣙⣿
⢰⣏⢉⡷⣽⣟⡇⣾⡇⣷⡷⢸⡦⣾⣽⢸⢌⣷⣹⣹⡷⡶⣿⢳⣾⣷⣾⣿⡆⢴⣱⣾⠂⢺⣿⡇⡷⣮⣏⣷
⣁⡀⡁⡀⢈⣉⡀⣀⡀⡩⡂⣁⡀⣀⢐⣁⠀⣉⣈⠀⣉⣉⣀⣩⣊⣅⣀⣉⡀⢈⣨⣀⡕⢍⣉⡁⢈⡀⠀⣀
⠿⠧⠧⠇⠽⠧⠗⠿⠇⠧⠇⠿⠯⠿⠿⠬⠥⠿⠼⠴⠯⠇⠿⠺⠿⠿⠿⠿⠧⠽⠿⠿⠇⠸⢿⢗⠆⠯⠤⠿
⢺⣍⣘⠜⣹⣋⡇⣯⡇⣟⡟⠨⢄⣮⣩⢛⣈⣽⣩⣙⣛⠜⢼⠢⢭⡭⢯⣽⡏⣸⡹⣯⠂⠰⡬⡅⣛⢜⣋⣭
⢸⡿⢸⡌⣿⡽⡇⣽⡇⣿⡟⢲⣤⣽⣿⣾⡾⣿⣿⡿⣯⡬⣼⢠⣼⣧⣽⣿⣷⣼⢯⣽⠀⢠⣤⡇⡏⣼⢿⣷

At this point, we can now write the code for the PageRank algorithm:

julia> using LinearAlgebraWARNING: using LinearAlgebra.normalize in module Main conflicts with an existing identifier.
julia> function pagerank(A; Niter=20, damping=.15)
           Nmax = size(A, 1)
           r = rand(1, Nmax);              # Generate a random starting rank.
           r = r ./ norm(r, 1);            # Normalize
           a = (1 - damping) ./ Nmax;      # Create damping vector
       
           for i=1:Niter
               s = r * A
               rmul!(s, damping)
               r = s .+ (a * sum(r, dims=2));   # Compute PageRank.
           end
       
           r = r ./ norm(r, 1);
       
           return r
       endpagerank (generic function with 1 method)

Using this function, we apply it to the above similarity matrix (sim_mat) and extract the PageRank scores for all verses. These scores will serve as the weights, and so higher scores suggest that the verse has a lot of connections to other verses in the corpus, which means it represents per se the corpus.

julia> p = pagerank(sim_mat)1×109 Matrix{Float64}:
 0.00258995  0.00258329  0.00269773  …  0.00630215  0.00256489  0.00239992

Now we sort these scores in descending order and use it to rearrange the original verses:

julia> idx = sortperm(vec(p), rev=true)[1:10]10-element Vector{Int64}:
  84
  88
  91
  65
  69
   7
  27
 107
  90
  67

Finally, the following 10 verses best summarizes the corpus (Chapter 18) using TextRank:

julia> verse_nos = verses(CorpusData(crpstbl), number=true, start_end=false)1-element Vector{Tuple{Vector{Int64}, Vector{Int64}}}:
 ([18], [1, 2, 4, 5, 6, 7, 8, 9, 10, 11  …  101, 102, 103, 104, 105, 106, 107, 108, 109, 110])
julia> verse_out = String[];
julia> chapter = Int64[];
julia> verse = Int64[];
julia> for v in verse_nos
           verse_out = vcat(verse_out, verses(crpsdata[v[1]][v[2]]))
           chapter = vcat(chapter, repeat(v[1], inner=length(v[2])))
           verse = vcat(verse, v[2])
       end
julia> using DataFrames
julia> tbl = DataFrame(
           chapter=chapter[idx],
           verse=verse[idx],
           verse_text=arabic.(verse_out[idx])
       );
julia> tbl10×3 DataFrame
 Row │ chapter  verse  verse_text
     │ Int64    Int64  String
─────┼───────────────────────────────────────────────────
   1 │      18     85  فَأَتْبَعَ سَبَبًا
   2 │      18     89  ثُمَّ أَتْبَعَ سَبَبًا
   3 │      18     92  ثُمَّ أَتْبَعَ سَبَبًا
   4 │      18     66  قَالَ لَهُۥ مُوسَىٰ هَلْ أَتَّبِعُكَ عَلَىٰٓ أَن تُعَلِّ…
   5 │      18     70  قَالَ فَإِنِ ٱتَّبَعْتَنِى فَلَا تَسْـَٔلْنِى عَن شَىْ…
   6 │      18      8  وَإِنَّا لَجَٰعِلُونَ مَا عَلَيْهَا صَعِيدًا جُرُزًا
   7 │      18     28  وَٱصْبِرْ نَفْسَكَ مَعَ ٱلَّذِينَ يَدْعُونَ رَبَّهُم بِ…
   8 │      18    108  خَٰلِدِينَ فِيهَا لَا يَبْغُونَ عَنْهَا حِوَلًا
   9 │      18     91  كَذَٰلِكَ وَقَدْ أَحَطْنَا بِمَا لَدَيْهِ خُبْرًا
  10 │      18     68  وَكَيْفَ تَصْبِرُ عَلَىٰ مَا لَمْ تُحِطْ بِهِۦ خُبْرًا

The following is the table of the above output properly formatted in HTML.

Pkg.add("Latexify")
using Latexify

mdtable(DataFrame(tbl), latex=false)
chapterverseverse_text
1885فَأَتْبَعَ سَبَبًا
1889ثُمَّ أَتْبَعَ سَبَبًا
1892ثُمَّ أَتْبَعَ سَبَبًا
1866قَالَ لَهُۥ مُوسَىٰ هَلْ أَتَّبِعُكَ عَلَىٰٓ أَن تُعَلِّمَنِ مِمَّا عُلِّمْتَ رُشْدًا
1870قَالَ فَإِنِ ٱتَّبَعْتَنِى فَلَا تَسْـَٔلْنِى عَن شَىْءٍ حَتَّىٰٓ أُحْدِثَ لَكَ مِنْهُ ذِكْرًا
188وَإِنَّا لَجَٰعِلُونَ مَا عَلَيْهَا صَعِيدًا جُرُزًا
1828وَٱصْبِرْ نَفْسَكَ مَعَ ٱلَّذِينَ يَدْعُونَ رَبَّهُم بِٱلْغَدَوٰةِ وَٱلْعَشِىِّ يُرِيدُونَ وَجْهَهُۥ وَلَا تَعْدُ عَيْنَاكَ عَنْهُمْ تُرِيدُ زِينَةَ ٱلْحَيَوٰةِ ٱلدُّنْيَا وَلَا تُطِعْ مَنْ أَغْفَلْنَا قَلْبَهُۥ عَن ذِكْرِنَا وَٱتَّبَعَ هَوَىٰهُ وَكَانَ أَمْرُهُۥ فُرُطًا
18108خَٰلِدِينَ فِيهَا لَا يَبْغُونَ عَنْهَا حِوَلًا
1891كَذَٰلِكَ وَقَدْ أَحَطْنَا بِمَا لَدَيْهِ خُبْرًا
1868وَكَيْفَ تَصْبِرُ عَلَىٰ مَا لَمْ تُحِطْ بِهِۦ خُبْرًا

The following are the translations of the above verses:

ChapterVerseEnglish Translation
1885So he travelled a course,
1889Then he travelled a ˹different˺ course
1892Then he travelled a ˹third˺ course
1866Moses said to him, “May I follow you, provided that you teach me some of the right guidance you have been taught?”
1870He responded, “Then if you follow me, do not question me about anything until I ˹myself˺ clarify it for you.”
188And We will certainly reduce whatever is on it to barren ground.
1828And patiently stick with those who call upon their Lord morning and evening, seeking His pleasure. Do not let your eyes look beyond them, desiring the luxuries of this worldly life. And do not obey those whose hearts We have made heedless of Our remembrance, who follow ˹only˺ their desires and whose state is ˹total˺ loss.
18108where they will be forever, never desiring anywhere else.
1891So it was. And We truly had full knowledge of him.
1868And how can you be patient with what is beyond your ˹realm of˺ knowledge?”