i
A amewo k o he Compa a i e analysis o ex
summa iza ion echniques
T iji Ghosh
Disse a ion p esen ed as pa ial equi emen o
ob aining he mas e ’s deg ee in Da a Science and
Ad anced Analy ics
2
Ins i u o Supe io de Es a ís ica e Ges ão de In o mação
Uni e sidade No a de Lisboa
A FRAMEWORK FOR THE COMPARATIVE ANALYSIS OF TEXT
SUMMARIZATION TECHNIQUES
by
T iji Ghosh
(M20170009)
Disse a ion p esen ed as pa ial equi emen o ob aining he mas e ’s deg ee in Da a Science
and Ad anced Analy ics
Ad iso / Co Ad iso : P o esso Rica do Rei; P o esso Robe o Hen iques
July 2021
3
ACKNOWLEDGEMENTS
I would i s like o hank my hesis ad iso P o esso Doc o Rica do Rei o he NOVA
In o ma ion Managemen School a Uni e sidade NOVA de Lisboa as he was he one who
challenged me o a heme as inno a i e as he one ha ga e mo o o his mas e ’s hesis on ex
summa iza ion. I wan o hank him o encou aging me and mo i a ing me e en when he ime
o de o e o his mas e ’s hesis was no wha was wan ed and expec ed.
Finally, a big hank you o my pa en s o encou aging me and gi ing me he chance o do his
mas e ’s in a eas as in e es ing and exci ing as ad anced analy ics a e. Tha ga e me he
possibili y o ha e a ca ee ha I ha e d eamed o . I would no be possible wi hou hei
suppo .
4
5
Con en s
1. In oduc ion ...................................................................................................................................... 8
1.1. Backg ound ............................................................................................................................... 8
1.2. Mo i a ion ................................................................................................................................. 8
1.3. Objec i e .................................................................................................................................... 8
2. EXTRACTIVE SUMMARIZATION ............................................................................................... 9
2.1. In e media e Rep esen a ion ............................................................................................ 9
2.2. Sen ence Sco e ......................................................................................................................... 9
2.3. Summa y Sen ences Selec ion ........................................................................................... 9
3. TOPIC REPRESENTATION APPROACHES .......................................................................... 11
3.1. Topic Wo ds .......................................................................................................................... 11
3.2. F equency-d i en App oaches ....................................................................................... 11
3.3. La en Seman ic Analysis ................................................................................................. 14
3.4. Bayesian Topic Models ...................................................................................................... 14
3.5. BERT ......................................................................................................................................... 15
4. THE IMPACT OF CONTEXT IN SUMMARIZATION ........................................................... 20
4.1. Web Summa iza ion ........................................................................................................... 20
4.2. Scien i ic A icles Summa iza ion ................................................................................. 20
4.3. Email Summa iza ion ........................................................................................................ 21
5. METHODOLOGY ........................................................................................................................... 35
5.1. Design Sea ch Resea ch .................................................................................................... 35
5.2. S a egy ................................................................................................................................... 37
6. PROPOSAL o a amewo k on scena ios o ex summa iza ion echniques ...... 38
6.1. PROPOSAL .............................................................................................................................. 38
6.2. VALIDATION .......................................................................................................................... 38
7. CONCLUSIONS ............................................................................................................................... 46
8. Re e ences ...................................................................................................................................... 47

6
Lis o Tables
Table 1 ............................................................................................................................................................... 21
Table 2 ............................................................................................................................................................... 27
Table 3 ............................................................................................................................................................... 38
Table 4 ............................................................................................................................................................... 42
Table 5 ............................................................................................................................................................... 43
7
Lis o igu es
Figu e 1 : Weigh ed Te ms /s Speci ici y .......................................................................................... 13
Figu e 2 : Be Embeddings ...................................................................................................................... 16
Figu e 3 : A chi ec u e o BERT .............................................................................................................. 17
Figu e 4 : Encode s and Decode s ......................................................................................................... 17
Figu e 5 : O e all p e- aining and ine- uning p ocedu es o BERT ..................................... 18
Figu e 6 : Fine Tuning phase .................................................................................................................... 18
Figu e 7 : Accu acy o BERTbase on Masked LM and Le - o-Righ ............................................. 19
Figu e 8 : P ecision and Recall o di e en ex iles ...................................................................... 40
Figu e 9 : P ecision, Recall and F-Measu e o di e en alues o k applying LSA ............ 41
Figu e 10 : O e all Compa ison o he me hods ............................................................................... 44
8
1. INTRODUCTION
1.1. BACKGROUND
We see ha wi h he boom o in o ma ion echnology and IOT (In e ne o hings), he size o
in o ma ion which is basically da a is inc easing a an ala ming a e. This in o ma ion can
always be ha nessed and i channeled in o he igh di ec ion, we can always ind meaning ul
in o ma ion. Bu he p oblem is his da a is no always nume ical and he e would be
p oblems whe e he da a would be comple ely ex ual, and some meaning has o be de i ed
om i . I one would ha e o go h ough hese ex s manually, i would ake hou s o e en
days o ge a concise and meaning ul in o ma ion ou o he ex . This is whe e a need o an
au oma ic summa ize a ises easing manual in e en ion, educing ime and cos bu a he
same ime e aining he key in o ma ion held by hese ex s. In he ecen yea s, new me hods
and app oaches ha e been de eloped which would help us o do so. These app oaches a e
implemen ed in lo o domains, o example, Sea ch engines p o ide snippe s as documen
p e iews, while news websi es p oduce sho ened desc ip ions o news subjec s, usually as
headlines, o make su ing easie .
B oadly speaking, he e a e mainly wo ways o ex summa iza ion – ex ac i e and
abs ac i e summa iza ion. Ex ac i e summa iza ion is he app oach in which impo an
sec ions o he whole ex a e il e ed ou o o m he condensed o m o he ex . While he
abs ac i e summa iza ion is he app oach in which he ex as a whole is in e p e ed and
examined and a e disce ning he meaning o he ex , sen ences a e gene a ed by he model
i sel desc ibing he impo an poin s in a concise way.
1.2. MOTIVATION
As he In e ne has g own in popula i y, a as amoun o in o ma ion has become a ailable.
Summa izing as amoun s o ex is challenging o humans. In his age o in o ma ion
o e load, au oma ic summa izing echnologies a e in high demand. We will y o ocus on
a ious ex ac ion me hodologies o single and mul i-documen summa iza ion in his hesis.
Some o he mos o en used me hods, such as opic ep esen a ion app oaches, equency-
d i en me hods, g aph-based and machine lea ning echniques, will be desc ibed. E en
hough i is di icul o ho oughly explain all o he many algo i hms and app oaches in his
hesis, we will y o gi e a good o e iew o ecen ends and b eak h oughs in au oma ic
summa izing me hods, as well as discuss he s a e-o - he-a and compa e a ious ways.
1.3. OBJECTIVE
The objec i e o his pape is o p o ide compa a i e analysis o di e en echniques o ex
summa iza ion used in di e en scena ios, me hodology - o de ine a se o analysis pa ame e
ha can allow us o classi y di e en echniques e.g., complexi y, accu acy and speed.
9
2. EXTRACTIVE SUMMARIZATION
Ex ac i e summa iza ion, as men ioned abo e, chooses pe inen subse s om he ex gi en,
based on some me ics and is hus combined o a condensed o m. To unde s and how
summa iza ion sys ems wo k, we desc ibe h ee, ai ly, independen asks which all
summa ize s pe o m:
1) Build a ansi ional depic ion o he inpu ex which communica es he mos impo an
aspec s o he ex .
2) Sco e he sen ences suppo ing he ep esen a ion.
3) Choose a summa y consis ing o a ie y o ex s.
2.1. INTERMEDIATE REPRESENTATION
All hese summa iza ion echniques will de elop some in e media e ep esen a ions o he
gi en inpu ex suppo ed ce ain me ics and disce n he impo an sen ences suppo ed hose
me ics. The e a e wo o ms o app oaches suppo ed he ep esen a ion: opic ep esen a ion
and indica o ep esen a ion.
Topic ep esen a ion me hods emodel he ex in o a ansi ional cha ac e iza ion and
examine he opic(s) gi en wi hin he ex . This me hod di e s in e ms o o mula ion and
he eby, complexi y, and a e di ided in o equency-d i en app oaches, opic wo d
app oaches, la en seman ic analysis and Bayesian opic models. We ake a deepe look in o
opic ep esen a ion app oaches wi hin he ollowing sec ions.
Indica o ep esen a ion app oaches desc ibe e e y sen ence as a lis ing o ea u es
(indica o s) o impo ance like sen ence leng h, posi ion wi hin he documen , ha ing ce ain
ph ases, e c.
2.2. SENTENCE SCORE
When he inpu ex is ans o med in o a o m which he model in e p e s, a sco e is assigned
o e e y sen ence based on ha me ic. This sco e is jus a ep esen a ion o how impo an a
sen ence is. These indica o s (me ic) a e de i ed om ma hema ical o machine lea ning
models. Once he sco e o e e y o he sen ences a e ob ained, hey a e, hen, agg ega ed o
ed in o a unc ion which anks hese sen ences based on he sco es ob ained. hey' e also
e e ed o as indica o weigh s.
2.3. SUMMARY SENTENCES SELECTION
A e he sen ences a e sco ed and anked suppo ed he a ious me ics o indica o s, he
impo an sen ences a e il e ed ou . These p ocesses can use di e en algo i hms o sepa a e
he sen ences suppo ed he anks and a ew o he sco es as an example edundancy sco e.
Some me hods use g eedy algo i hms o sea ch ou he simples sen ences ep esen ing he
essence o he ex . This me hod will no always be he mos e ec i e app oach which igno es
16
Figu e 2 : Be Embeddings
The inpu ex is i s ed in o he ex p ocessing embeddings namely: -
Posi ion Embeddings
Segmen Embeddings
Token Embeddings

17
Figu e 3 : A chi ec u e o BERT
The ou pu o hese embedding is hen ed o he BERT laye which consis s o ans o me s.
Figu e 4 : Encode s and Decode s
A ans o me can consis o 12/24 blocks o encode s wi h 12/16 a en ion heads and 110/340
million pa ame e s, namely BERTbase/BERTla ge espec i ely. I looked closely, each
ans o me can be a se o encode s and decode s as shown abo e in he diag am. I is he e
ha he ou pu o he ans o me is ed o he summa iza ion laye .
P e- aining has been qui e c ucial when i came o language models. The e ha e been
18
applica ions o hese models such as na u al language in e ence and pa aph asing.
This pape (De lin, Chang, Lee, & Tou ano a, 2018), mainly alks abou ine uning app oach
wi h he p oposal o BERT as desc ibed ea lie . The eason i is called bidi ec ional is because
unlike o he models whe e he ex is ead om le o igh o om igh o le , he BERT
app oach eads he sen ences in bo h he di ec ions and ies o unde s and he con ex o he
wo ds bo h o i s le and igh .
Figu e 5 : O e all p e- aining and ine- uning p ocedu es o BERT
In he abo e diag am, he a chi ec u e o he BERT is shown whe e apa om he ou pu
laye s, bo h use he same a chi ec u e.
Figu e 6 : Fine Tuning phase
Sou ce: (De lin, Chang, Lee, & Tou ano a, 2018)
The abo e igu e shows ha by wo king on he ine- uning phase o he model, he model was
able o achie e an accu acy by an o e whelming ma gin o 4.5% and 7% p io o i s s a e o
he a .
19
Figu e 7 : Accu acy o BERTbase on Masked LM and Le - o-Righ
Sou ce: (De lin, Chang, Lee, & Tou ano a, 2018)
Al hough i is o be men ioned ha his bi-di ec ional app oach akes longe han
unidi ec ional app oach which is wha has been shown in he abo e diag am whe e he BERT
MLM me hod con e ges slowe han le o igh bu accu acy wise, i is well ahead o he
o he app oach.
This bi-di ec ional app oach is, de ini ely, use ul al hough a bi slow and makes he
applica ion o BERT o a wide ange o ields.
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4. THE IMPACT OF CONTEXT IN SUMMARIZATION
I is qui e e iden ha a con ex is e y use ul when i comes o unde s anding he in o ma ion
p esen ed in a ex . In he same way, i models can be empowe ed wi h he in o ma ion o
con ex , hen a summa ize sys em would be able o p une he co ec in o ma ion o
summa ize he ex . Fo example, acco ding o (Allahya i, e al., 2017), when summa izing
blogs, he deba es o commen s ha ollow he blog pos a e use ul esou ces o de e mining
which po ions o he blog a e c i ical and in iguing. The e is a signi ican quan i y o
in o ma ion a ailable in scien i ic pape summa ies, such as published a icles and con e ence
in o ma ion, ha can be used o highligh essen ial sen ences in he o iginal wo k.
4.1. WEB SUMMARIZATION
I we look a he web pages, we will see ha hey ha e lo o objec s which is no always
possible o summa ize o example, pic u es, gi s, and some unwan ed ma e ials like
ad e isemen s which is so no ele an o he in o ma ion gi en in he pages. Fo hose kinds
o si ua ions, i will be help ul o use he links which di ec us o he page o be summa ized.
These links would gi e he model a knowledge o he con ex and would be help ul o p o ide
imp o ed summa y o he page. In (Ami ay & Pa is, 2000), whe e hey alk abou websi e
summu iza ion o he i s ime, hey came up wi h a concep called “THE INCOMMONSENSE
SYSTEM”. This model inco po a es a web c awling sys em which c awls all he links ha link
back o he cu en page and his is how he con ex is de i ed. Since hen, a lo o di e en
algo i hms ha e been de eloped which was based on he abo e p inciple bu has beeen
imp o ed.
4.2. SCIENTIFIC ARTICLES SUMMARIZATION
In (Mei & Zhai, 2008), a summu iza ion p oblem ela ed o scien i ic pape s is s udied and
p esen ed. Needless, o say wi h each passing yea , new disco e ies a e being made and new
esea ch pape s a e being published e e y yea . So wi h his, he daun ing ask o b ie ing a
scien i ic pape s becomes eally challenging. Specially when i comes o including a con ex
in he summa y which e e ences a mul i ude o di e en esea ch pape s. In o de o sol e
his p oblem, hey came wi h a concep o impac based summu iza ion as men ioned in (Mei
& Zhai, 2008). This me hod le e ages on he impo ance o sen ence sco e which has been
used in he o iginal pape using he KL di e gence me hod (i.e., inding he simila i y
be ween a sen ence and he language model). The conclusions made in his pape a e
subs an ial po ing ha his me hod was use ul and could be used o u u e b ei ing models
o scien i ic pape s.They p oposed a language model ha gi es a p obabili y o each wo d in
he ci a ion con ex sen ences. They hen sco e he impo ance o sen ences in he o iginal
pape .
.
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4.3. EMAIL SUMMARIZATION
When i comes o email summa iza ion, he ex s become a bi di e en . In o de o ind a
con ex , a whole chain mail h eads a e o be p ocessed o de e mine he s o y. In (Nenko a &
Bagga, 2003), hey discuss a ew me hods o how he con e sa ional na u e o he ex can be
used o ga he con ex . Thei me hods does p o ide a conclusi e e idence as o how use ul
his me hod could be and his u he , his me hod could be enhanced by using some
isualiza ion echniques as well. While in (Rambow, Sh es ha, Chen, & Lau idsen, 2004),
hey ake a bi o a di e en app oach whe e hey de e mine impo an ea u es o
summa izing he email, whe e each ea u e could be one h ead o se e al h eads o email.
(Newman & Bli ze , 2003) discusses a whole new app oach o summa izing an email. They
le e age he clus e ing algo i hm o sol e he p oblem o email summa iza ion. In hei pape
hey alk abou ew clus e ing algo i hms which help hem see all he h eads o he email as a
whole and pos applica ion o he men ioned algo i hms, an o e iew is o med.
Below, Table 2. ci e he jou nals o he e e ences om which some echniques we e analyzed
and u he mo e, hei bene i s o de ec s a e gi en. Table 2. Illus a es he a ious me hods
which we e explained in he a icles men ioned in Table 1

22
21
Table 1
Jou nal/Con e ence
Sub Topic
Yea s
(xxxx-
xxyy)
# A icles
#A icles
Techniques Bene i s
#A icles
Techniques D awbacks
2017 In e na ional
Con e ence on
Compu ing
Me hodologies and
Communica ion
(ICCMC)
Tex
Summa iza ion
2017
Au oma ic ex
summa iza ion
by local
sco ing and
anking o
imp o ing
cohe ence
I has made use o sen ence ea u e
me ics o sco e sen ences like “sen ence
o sen ence cohesion”, “wo d equency”,
i makes use o me ics which migh igno e
aluable in o ma ion and he eby making he
summa y less meaning ul.
Fo example, me ic like “leng h o sen ence”
is used o a oid selec ing oo sho o oo
long sen ences o he documen . Doing his,
a imes, he model migh o e look some
in o ma ion which migh ha e been p esen
in hose sen ences bu we e no aken in o
accoun while summa izing he ex .
22
2017 In e na ional
Con e ence on Big
Da a, IoT and Da a
Science (BID)
Tex
Summa iza ion
2017
Au oma ic ex
summa iza ion
o news
a icles
The lexical chain gene a ion p oposed
by Silbe and McCoy algo i hm has
linea un ime complexi y. Fu he ,
ce ain issues we e esol ed in bo h
algo i hms by
implemen ing p onoun esolu ion and
enhanced sen ence sco ing o le e age
he s uc u e o news a icles.
One o he lexical chain gene a ion algo i hm
adop ed was p oposed by
(Ba zilay & Elhadad, 2000) has exponen ial
un ime complexi y.
A i icial In elligence
Re iew a chi e
Volume 47 Issue 1,
Janua y 2017 Pages 1-
66
Tex
Summa iza ion
2017
Recen
au oma ic ex
summa iza ion
echniques: a
su ey
This pape alks abou a a ie y o
echniques which ha e hei own
bene i s. 1. T ained Summa ize and
La en Sema ic Analysis – uses a
modi ied co pus-based app oach and a
TRM echnique based on la en seman ic
analysis. The summa ize is based on a
unc ion ha assesses main
sen ences/wo ds o hings like loca ion,
keywo d, likeness o i le, and cen ali y
in o de o gene a e summa ies. The
sco e unc ion is op imized using
s ochas ic echniques such as gene ic
algo i hms. 2. In o ma ion e ie al
pe o mance has g ea ly imp o ed
The e a e some d awbacks o he app oaches
used which a e as ollows:T ained
Summa ize and La en Sema ic analysis –
he summa ies gene a ed a e no e y
consis en wi h opic and he sen ences don’
co ela e so much a imes. Fea u e weigh s
o sco e unc ion p oduced by GA ail o
consis en ly gi e he bes esul s o he es
co pus. Ob aining he app op ia e dimension
educ ion a io and explaining LSA e ec s a e
ough in he LSA+TRM echnique. Mo eo e ,
he ime complexi y o compu e SVD is qui e
high. 2. Using a sen ence-based abs ac ion
echnique o ex ac da a – In his app oach,
only casual cohe ence is conside ed whe eas
23
because o he use o a sen ence-based
abs ac ion echnique. Sen ences ha
ep esen he cen al no ion a e linked.
3. Unde s anding and summa izing
documen s using a documen concep
la ice – In compa ison o exis ing
sen ence g ouping and sen ence sco ing
algo i hms, he sugges ed app oach
pe o ms excep ionally well. 4. Sen ence
ex ac ion using ex summa iza ion
based on con ex and s a is ics – This
space and ime which a e in e - ela ed links
which makes sense ou o he documen as a
whole a e also equi ed o ep esen ing
beha io al con ex . 3. Th ough he use o a
documen concep la ice, i is possible o
comp ehend and summa ize ex – ime
complexi y o gene a ing a DCI is high
because i conside s all possible
combina ions. 4. Sen ence ex ac ion h ough
con ex ual and s a is ical based
summa iza ion ex .
30
documen
summa ies
h ough non -
nega i e
ma ix
ac o iza ion
his app oach could no na u ally ca ch he meaning o seman ic ea u es ha a e
highly spa se and ha e a limi ed iew o meaning. As a esul , summa iza ion sys ems
based on LSA a e unable o choose meaning ul ph ases. As a esul , elemen s o
seman ic ea u e ec o s in he sugges ed me hod exclusi ely con ain non-nega i e
alues and a e also ex emely spa se, allowing seman ic cha ac e is ics o be easily
ead. A sen ence can be ep esen ed by a linea combina ion o ce ain signi ican
seman ic elemen s. As a esul , sub opics in a documen can be quickly iden i ied, and
he e's a be e p obabili y o ex ac ing ele an lines. A me hod o picking ph ases
o cons uc gene al documen summa ies is sugges ed using NMF, in which a con en
is i s p e-p ocessed and hen summa ized. To gene a e a non-nega i e seman ic
ea u e ma ix, NMF is used o a e m-by-sen ence ma ix. Fo each sen ence, gene ic
ele ance is calcula ed, which indica es how much a sen ence explains.
Que y based
summa iza ion
o mul iple
documen s by
applying
eg ession
models
2011
Ouyang Y
Ouyang, Li, Li, &
Lu, 2011
(Ouyang, Li, Li, & Lu, 2011) sugges ed a me hod o anking ph ases in que y-based
summa iza ion o nume ous manusc ip s using eg ession models. Th ee que y-
dependen ea u es, such as named-en i y ma ching, wo d-ma ching, and seman ic
ma ching, and ou que y-independen ea u es, such as sen ence posi ion, named
en i y, wo d TF-IDF, and s op-wo d penal y, a e used in his me hodology o choose
main sen ences in que y-based summa iza ion o mul iple documen s. To begin wi h,
human summa ies gene a e " alse" aining da a. Then, using di e en me hods based
on he N-g am me hodology ha calcula e "nea ly ue" ele ance a ings o ph ases
a e c ea ed and analyzed using his aining da a and hei collec ion o ex s, and a
mapping unc ion is lea ned using his aining da a ia a collec ion o p e iously
speci ied ea u es o sen ences. Then, using his lea ned unc ion, he signi icance o
sen ences in he es da a is es ima ed. An e icien da a collec ion o aining da a o
lea ning eg ession models equi es wo hings: (a) an app op ia e g oup o opics
wi h co ec ly handw i en summa ies, and (b) a good app oach o compu ing he
ele ance o wo ds. The Maximal Ma ginal Rele ance (MMR) echnique is used o
emo e edundancy om he summa y.
Au oma ic ex
summa iza ion
using MR, GA,
2009
Mohamed Abdel
Fa ah, Fuji Ren
(Fa ah and Ren
2009)
Wi h he use o a ew s a is ical ea u es, (Fa ah & Ren, 2009) sugges ed an app oach
o imp o e con en selec ion in au oma ic ex summa iza ion. As a ainable
summa ize , his me hod elies on dis inc s a is ical aspec s in each sen ence o
gene a e summa ies. Posi ion o Sen ence (Pos), + e keywo d, - e keywo d, + e

31
FFNN, GMM
and PNN based
models
keywo d, + e keywo d, + e keywo d, + e keywo d, + e keywo d, + e keywo d, + e
keywo d, + e keywo d, + e keywo d, R2T, Cen ali y o Sen ence (Cen), P esence o
Name En i y in Sen ence (PNE), P esence o Numbe s in Sen ence (PN), Bushy Pa h o
Sen ence (BP), Rela i e Leng h o Sen ence (RL), and Agg ega e Simila i y (AS) a e all
measu es o sen ence simila i y. Gene ic Algo i hm (GA) and Ma hema ical Reg ession
(MR) models ha e been ained o acqui e an op imal mix o ea u e weigh s by
mixing all o hese ea u es. Fo sen ence ca ego iza ion, eed o wa d neu al
ne wo ks (FFNN) and p obabilis ic neu al ne wo ks (PNN) a e u ilized. Some ex
ea u es, such as he + e and - e keywo ds, a e language-dependen , while eigh
o he s a e no . All o he abo e-men ioned a iables a e aken in o accoun when
calcula ing a sen ence's weigh ed sco e unc ion. All sen ences in a documen a e
anked in dec easing o de o hei sco es, and a highly sco ed clus e o sen ences is
u ilized o gene a e a summa y o he con en using a ious comp ession a es (10,
20, 30 pe cen used he e). Conclusion: The esul s demons a e ha ea u e BP is he
mos essen ial ex ea u e since i p oduces he bes esul s, while ea u e PND
p oduces he wo s esul s because s a is ical da a is absen om eligious and
poli ical pieces. Because i could model a bi a y densi ies, he GMM app oach
p oduced he bes esul s o all he s a egies.
Maximum
co e age and
minimum
edundancy in
summa iza ion
o ex
2011
Algulie , R. M
Algulie ,
Aliguliye ,
Haji ahimo a, &
Mehdiye , 2011
(Algulie , Aliguliye , Haji ahimo a, & Mehdiye , 2011) in oduced an unsupe ised
summa izing model o gene ic ex as an In ege Linea P og amming p oblem (ILP)
ha immedia ely de ec s essen ial sen ences om he a icle as well as he ull
a icle's ele an in o ma ion. Maximum Co e age and Minimum Redundancy is he
name o his s a egy (MCMR). This me hod aims o imp o e h ee key aspec s o a
summa y: (a) ele ance, (b) edundancy, and (c) leng h. A subse o sen ences om
he documen collec ion's ele an ex is picked. Then, using NGD-based simila i y
(No malized Google Dis ance) and cosine simila i y, simila i y be ween he summa y
and he documen collec ion is compu ed, and his simila i y mus be maximized. An
objec i e unc ion is de eloped and mus be maximized o ensu e ha he summa y
con ains he impo an con en ound in he documen collec ion and ha he
summa y does no con ain a signi ican numbe o ph ases ha communica e he
same in o ma ion. A he same ime, he leng h o he summa y mus be limi ed.
Las ly, an empi ical unc ion is c ea ed by linea ly combining he cosine simila i y-
32
based and NGD-based simila i y empi ical unc ions, and his combined empi ical
unc ion mus be maximized. This echnique o summa izing is inco po a ed as an
op imiza ion p oblem ha aims o ind a global solu ion o he p oblem. The B anch &
Bound algo i hm (B&B) and he Bina y Swa m Op imiza ion me hod a e he
algo i hms used o add ess he ILP p oblem. Conclusion: This me hod, which
combines MCMR wi h he B&B algo i hm, su passes all o he s. I demons a es ha
summa izing ou comes a e dependen on simila i y measu emen s. I is also p o ed
h ough es s ha using cosine simila i y and NGD-based simila i y me ics oge he
p oduces be e esul s han using hem sepa a ely.
Summa iza ion
o documen s
h ough a
p og essi e
echnique o
selec ion o
sen ences
2013
Ouyang Y
Ouyang, Li, Zhang,
Li, & Lu, 2013
(Ouyang, Li, Zhang, Li, & Lu, 2013) p oposed a new p og essi e echnique o
gene a ing a summa y based on he selec ion o "no el and salien " sen ences.
Subsuming ela ionship be ween wo sen ences, i.e., an i egula ela ionship be ween
sen ences ha shows he le el o ecommenda ion o one ph ase by ano he . In o de
o asce ain he link be ween wo ph ases, he ela ionship be ween hei concep s
mus be ound. The associa ion be ween concep s is hen disco e ed by using a
co e age-based measu e o disco e he ela ionship be ween wo ds. A Di ec Acyclic
G aph (DAG) is used o o ganize all o he wo ds ha appea in he ound wo d
ela ions. A p og essi e s a egy o sen ence selec ion is c ea ed on he basis o an
asymme ic ela ionship be ween sen ences, in which a sen ence is ei he picked as a
no el gene al s a emen o as a suppo ing sen ence. The ollowing wo me hods a e
used o choose new and ele an sen ences in his me hod: (a) disco e ed concep s
a e only included du ing he assessmen o sen ence ele ance o assu e sen ence
o iginali y, and (b) o now, he ela ionship be ween sen ences is used o imp o e he
saliency measu e. To implemen his s a egy, a andom walk on he DAG om he
cen al node o i s nea by nodes is pe o med, wi h he goal o co e ing he cen al
wo ds i s and hen eaching he g ea es amoun o wo ds ia wo d ela ions.
Redundancy is elimina ed by punishing epe i i e wo ds, esul ing in esh concep s
being in oduced each ime a new ph ase is chosen. Conclusion: In e ms o gene a ing
summa ies wi h imp o ed saliency and co e age, he P og essi e sys em su passes
he adi ional Sequen ial app oach.
E alua ion o
2013
Fe ei a, Ra ael;
Cab al, Luciano de
Fe ei a, e al.,
In he ecen decade, (Fe ei a, e al., 2013) inco po a ed i een sco ing echniques
ha had been e e enced in he esea ch. ROUGE (Lin 2004) is used o quan i a i e
33
sen ence
sco ing
me hods o
ex ac i e
summa iza ion
o ex
Souza; Lins, Ra ael
Duei e; Sil a,
Gab iel Pe ei a e;
F ei as, F ed;
Ca alcan i, Geo ge
D.C.; Lima,
Rinaldo; Simske,
S e en J.; Fa a o,
Luciano
20132013
e alua ion, while he numbe o sen ences ha a e simila among he machine-
gene a ed and human-made summa ies is coun ed o quali a i e e alua ion. The
p ocessing ime o each algo i hm is aken in o accoun . Wo d sco ing, sen ence
sco ing, and g aph sco ing app oaches a e used o pick ele an sen ences. The mos
essen ial e ms a e gi en sco es in he wo d sco ing app oach. Wo d equency,
TF/IDF, uppe case, p ope noun, wo d co-occu ence, and lexical simila i y a e
among he app oaches used o sco e wo ds. The p ope ies o sen ences a e examined
in he sen ence sco ing app oach. The exis ence o cues, nume ical da a, sen ence
leng h, sen ence posi ion, and sen ence cen ali y a e all ac o s in sen ence sco ing.
Sco es a e de e mined using he g aph sco ing app oach by looking a he
ela ionships be ween sen ences. Tex ank, bushy pa h o he node, and agg ega e
simila i y a e all g aph sco ing app oaches. The six common conce ns o s op wo ds,
s uc u al ans o ma ion, compa able seman ics, ambigui y, edundancy, and co-
e e ence a e hen explo ed, along wi h some sugges ions o ad ancing sen ence
sco e ou comes.
Explo ing
co ela ions
among
mul iple e ms
h ough a
g aph-based
summa ize ,
GRAPHSUM
2013
Ba alis, Elena;
Caglie o, Luca;
Maho o, Naeem;
Fio i, Alessand o
Ba alis, Caglie o,
Maho o, & Fio i,
2013
GRAPHSUM, a new g aph-based, gene al-pu pose summa ize o summa izing
nume ous documen s, was p oposed by (Ba alis, Caglie o, Maho o, & Fio i, 2013).
This me hod in es iga es and applies associa ion ules, a da a mining me hodology o
inding connec ions be ween se e al e ms. I is no elian on sophis ica ed seman ic
models (like axonomies o on ologies). The documen collec ion is o ganized as a
ansac ional da ase a e p ep ocessing so ha associa ion ule mining may be
conduc ed on i . Then, om he ansac ional da ase , equen ly ecu ing i emse s
wi h high co ela ions among he e ms a e iden i ied, and a co ela ion g aph is
cons uc ed om hese e ms, which will aid in he selec ion o signi ican lines o he
summa y. The Ap io i algo i hm is used o mine equen ly ecu ing i emse s, and
he suppo measu e is employed o his job. The li measu e indica es he in ensi y
o ela ionship be ween wo e ms and is used o e alua e posi i e o nega i e
connec ions be ween commonly used wo ds. A a ia ion o he classic PageRank
g aph anking algo i hm is used o de e mine he ele ance o he g aph nodes. The
g aph nodes ha ha e a signi ican numbe o posi i e co ela ions a e placed i s ,
while hose ha ha e a nega i e connec ion wi h he adjacen nodes a e penalized.
Fo summa y c ea ion, he sen ences ha a e he mos app op ia e o he co ela ion
34
g aph and ha e a high ele ance sco e a e picked. The g eedy algo i hm is employed
o selec sen ences in his case. GRAPHSUM pe o ms be e o e a wide ange o
s a e-o - he-a echniques, some o which ely hea ily on highly de eloped seman ic-
based models o complica ed language p ocesses.
Inco po a ing
a ious le els
o language
analysis o
ackling
edundancy in
ex
summa iza ion
2013
Elena Llo e ,
Manuel Paloma
Llo e & Paloma ,
2013
(Llo e & Paloma , 2013) p o ided a me hod o de ec ing edundan in o ma ion
based on h ee laye s o language analysis: lexical, syn ac ic, and seman ic. Cosine
simila i y is u ilized in he lexical based echnique o de ec simila i y be ween
sen ences in wo sou ces. Those sen ences ha ha e a cosine simila i y g ea e han a
ce ain h eshold a e conside ed epe i i e, and hey a e all elimina ed. In a syn ac ic-
based me hod, en ailmen ela ions a e compu ed be ween pai s o ph ases o
de e mine whe he he meaning o one sen ence can be deduced om he meaning o
he o he sen ence. I a posi i e en ailmen is ob ained, he second sen ence is deemed
supe luous and elimina ed. Sen ence alignmen is de e mined a he documen le el
be ween a se o linked documen s using a open sou ce a ailable Champollian Tool Ki
in a seman ic-based manne . Syn ac ic and seman ic echniques a e p e e able han
lexical app oaches ha ely on cosine simila i y. Tex summa iza ion can be done in
wo ways. Be o e he ma e ial is summa ized, unnecessa y sen ences a e dele ed in
he i s echnique. The se o use ul sen ences is hen gi en o he summa iza ion
sys em, which uses s a is ical ( e m equency) and linguis ic (code quan i y
p inciple) ac o s o selec essen ial sen ences, as well as a summa y.
35
5. METHODOLOGY
This s udy will be conduc ed h ough a quali a i e esea ch, based on a well-s uc u ed
pa ame e o compa ing he di e en echniques adop ed o ex summa iza ion echniques.
This is he basis o compa e di e en echniques and shed ligh on which echniques would be
mo e use ul in (i any) pa icula si ua ions. Wha a e hei d awbacks and ad an ages?
5.1. DESIGN SEARCH RESEARCH
Design Science Resea ch is a o m o in es iga ion ha en ails building o imp o ing
some hing in a no el way in esponse o a speci ic challenge.
The ques o a solu ion based on ex ensi e scien i ic in es iga ion ensu es ha he inal
p oposed a i ac is cohe en and c edible. A c ucial phase ha should no be o e looked is
good communica ion o he inished p oduc (He ne , Ma ch, Pa k, & Ram, 2004).
Each o he six key s ages o DSR me hodology, as shown in Figu e 5, will be discussed in
g ea e de ail igh away.
Figu e 3. DSR Me hod Adap a ion (Pe e s, Tuunanen, Ro henbe ge , & Cha e jee, 2007)
Iden i y p oblem and mo i a ion
De ine he esea ch challenge in de ail and jus i y he impo ance o a solu ion.
Begin by es ablishing a es able heo y ha leads o a esea ch p oblem by demons a ing o
s akeholde s he alue o an e ec i e solu ion and wha hey will gain om i s esul (Pe e s,
Tuunanen, Ro henbe ge , & Cha e jee, 2007).
De ine objec i es and a solu ion

36
Clea ly de ine goals (quan i a i e o quali a i e) o es ablish he ounda ion o a solu ion
based on he p oblem cha ac e iza ion and wha can and canno be done (Pe e s, Tuunanen,
Ro henbe ge , & Cha e jee, 2007).
Design and De elopmen
The goal o he design and de elopmen s ages is o c ea e knowledge h ough he design and
de elopmen o he a i ac i sel (G ego & He ne , 2013). This could be accomplished by
b eaking down he majo scien i ic p oblem in o smalle componen s (He ne , Ma ch, Pa k,
& Ram, 2004). To ha e an e ec i e/ clea s uc u e in he nex phase, i is necessa y o ha e a
clea g asp o he solu ion alue and o de end i wi h some heo e ical ounda ion (Pe e s,
Tuunanen, Ro henbe ge , & Cha e jee, 2007). A solu ion ha mus mee business
equi emen s (He ne , Ma ch, Pa k, & Ram, 2004).
To gain he app op ia e heo e ical basis, i is essen ial o do esea ch and collec knowledge
abou he p esen s a us o he p oblem and exis ing solu ions, as well as o analyze di ec and
indi ec solu ions and hei e icacy (Pe e s, Tuunanen, Ro henbe ge , & Cha e jee, 2007).
Wi h he knowledge, i is possible o de elop a solu ion o mee esea ch and, as a esul ,
business objec i es, as well as o deba e he use ulness o he sugges ed a i ac (He ne ,
Ma ch, Pa k, & Ram, 2004).
E alua ion
To ce i y an a i ac 's e icacy, i mus be pu o use o p esen ed o s akeholde s (Pe e s,
Tuunanen, Ro henbe ge , & Cha e jee, 2007), which mus be suppo ed by a clea
speci ica ion o e alua ion me hodologies ha a e sui able o he si ua ion a hand and a e
based on indus y equi emen s. Because he majo i y o claims on he inal solu ion a e
ela ed o pe o mance issues, alignmen wi h business needs is c i ical (He ne , Ma ch, Pa k,
& Ram, 2004).
Compa ing wha alls unde he pu iew o he mas e 's hesis wi h wha could be obse ed in
i s p ac ical implemen a ion is one echnique o e alua e how he answe ma ches he ini ial
challenge (Pe e s, Tuunanen, Ro henbe ge , & Cha e jee, 2007).
Al hough i is c i ical o emphasize ha he p ima y goal is o "iden i y how well an a i ac
wo ks" a he han " heo ize o p o e any hing abou why he a i ac wo ks" (He ne , Ma ch,
Pa k, & Ram, 2004).
A he end o his phase, i should be de e mined whe he he a i ac is eady o be sha ed
wi h he es o he wo ld, o whe he mo e e o should be spen imp o ing i o make i
mo e e ec i e/aligned wi h he o iginal p oblems (Pe e s, Tuunanen, Ro henbe ge , &
Cha e jee, 2007).
Communica ion
While eleasing he inal a i ac o he public is a s ep in he igh di ec ion, i 's also c i ical o
le people know how unique and success ul he a i ac is in sol ing he highligh ed p oblems
(Pe e s, Tuunanen, Ro henbe ge , & Cha e jee, 2007). I is c i ical o discuss how he
a i ac was c ea ed and he e iew p ocess ha led o i s alida ion h oughou his
communica ion (He ne , Ma ch, Pa k, & Ram, 2004).
37
I should be con eyed o echnical and managemen audiences in o de o ga he inpu o
enhance he solu ion, bo h in e ms o business and echnology, o u u e implemen a ions
(He ne , Ma ch, Pa k, & Ram, 2004).
5.2. STRATEGY
P oblem
The e a e nume ous ex summa izing app oaches, each wi h i s own se o bene i s and
d awbacks. Some a e mo e compu a ionally complex han o he s, while o he s ha e only been
implemen ed in speci ic languages. Some u ilize mo e s a is ical measu es o quan i a i ely
add ess summa izing p oblems, while o he s mo e ex ac i e in na u e. Despi e all o hese
possibili ies, he e is no single app oach o me hodology ha can be used on any ype o ex .
We need o know which s a egy o echnique o use in a ious si ua ions.
Objec i e
A e s a ing he opic, ou goal in his pape will be o esea ch and assess se e al s a egies,
as well as o desc ibe hei bene i s and d awbacks, as well as he si ua ions and ci cums ances
in which hey migh be employed. In he same case, no all me hods would pe o m he same.
As a esul , we would do ou bes o p oduce a ai compa ison and highligh he echniques'
o me hodology' limi a ions.
Design and De elopmen
Ini ially, a numbe o esea ch publica ions on ex summa iza ion app oaches we e examined.
Some o he s a egies o examining i s algo i hm, ime complexi y, he da a i was
implemen ed on, how e icien he algo i hm is, how use ul he gene a ed summa y is, and
whe he i was an abs ac i e o ex ac i e based me hodology ha e been de ailed in dep h
abo e.
38
6. PROPOSAL OF A FRAMEWORK ON SCENARIOS OF TEXT SUMMARIZATION TECHNIQUES
6.1. PROPOSAL
Al hough ex summa iza ion has a as numbe o echniques o o e , i was no possible o co e all o hose he e and a such only ew we e
selec ed, which we e s udied he e a o emen ioned in he abo e ables. The ollowing able below compa es hose abo e echniques in e ms o
accu acy and ime complexi y, applicabili y. Al hough his able does no gi e a ai compa ison since, all hese echniques we e no applied on he
same documen and o he same si ua ions.
Table 3
Techniques
Pa ame e s
Accu acy
Speed
Applicabili y on
di e en language
Scena ios applicable
The lexical chain
gene a ion
Accu acy is be e
Has linea un ime
complexi y
Fo example,
Bengali
Al hough his me hod can be
applied o mul iple si ua ions,
mos esea ch pape s s a e i s
main applicabili y in Wo ld
Wide Web.
La en Seman ic
Analysis
Ce ain combina ions
show di e en
accu acy men ioned
below
Linea Time
complexi y
Fo example,
Bengali, Hindi
LSA now scales o ca. 100
million-wo d co po a by la ge
compu e memo y and new
algo i hms.
Que y based
summa iza ion o
mul iple documen s by
Resul s demons a e
ha o compu ing
he impo ance o In
The speed a ies
wi h documen s
explained in de ail
Al hough, any pape
ela ed o his
echnique has no
summa izing esea ch pape s
o a speci ic domain, biomedical
documen s o be e accu acy
39
applying eg ession
models
compa ison o
aining o sco e and
classi ying models,
eg ession models
pe o m be e .
below
ye been applied o
o he language. Bu
his echnique
should no ha e any
issues ( echnical) i
applied o o he
language.
In summa izing ex ,
maximum scope and
desi ed minimal
epe i ion
Accu acy is 97%
acco ding o
(HoudaOu aida,
Oma Nouali, &
PhilippeBlache,
2014). Al hough his
is jus one sample.
Compu a ional ime
is p opo ional o
O(X*Y) whe e X and
Y a e di e en e ms
in he dis ance
ma ix used o
disce n he
simila i y.
Tes ed in languages
like A abic, Czech,
English, F ench,
G eek, Heb ew and
Hindi
single- and mul i-documen
summa iza ion. In bo h asks,
documen s a e spli in o
sen ences in p ep ocessing
E olu iona y
op imiza ion algo i hm
o summa izing
mul iple documen s
Accu acy is usually
good i he algo i hm
is un making su e
ha he whole sea ch
space is co e ed and
no s uck a local
maxima
Time complexi y o
hese algo i hms is
usually p e y high
as i has o make
su e ha he whole
sea ch space is
co e ed du ing he
un- ime.
This p oposed
me hod has no ye
been applied in
o he languages.
Digi al a chi es o
go e nmen al documen s
The lexical chain gene a ion - Wo d Sense Disambigua ion (WSD) accu acy is be e . The algo i hm p oposed by Silbe and McCoy has linea un
ime complexi y. Tes ed in di e en languages apa om English. Fo example, Bengali. Al hough his me hod can be applied o mul iple si ua ions,
46
7. CONCLUSIONS
As he In e ne has g own in popula i y, a as amoun o in o ma ion has become a ailable.
Summa izing as amoun s o ex is challenging o humans. In his age o in o ma ion o e load,
au oma ic summa izing echnologies a e in high demand.
Va ious ex ac ion me hodologies o single and mul i-documen summa iza ion we e
highligh ed in his esea ch. Topic ep esen a ion app oaches, equency-d i en me hods, g aph-
based and machine lea ning echniques we e desc ibed as some o he mos o en u ilized
me hodologies. Al hough i is impossible o elucida e all o he many me hods and app oaches in
my hesis, i does p o ide a good o e iew o ecen ends and ad ancemen s in au oma ic
summa izing me hods and desc ibes he cu en s a e-o - he-a in his ield.
Limi a ions
One o he main limi a ions o his epo is ha i wasn’ alida ed by lo o people gi en he
ewe numbe o expe s in his ield. Wi h ha goes he unsaid, ha his pape doesn’
documen all he NLP echniques, which is qui e a b oad ield.

47
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