8/14/2019 Update - 26 Jan 2009

1/23

ProductReviewSummarization

LyDuy Khang

8/14/2019 Update - 26 Jan 2009

2/23

Recall:

Summarizationsystem

.

2. Productfacet

identification

3. Facetoriente sentencec ustering

4. Infocused

snippet

summarization

8/14/2019 Update - 26 Jan 2009

3/23

Recall:

Productfacet

identification

(1 2)

Statisticalmeasurement

(ARM)

to

extract

frequent

Noise+Facetcoverage+noimplicitfacets

8/14/2019 Update - 26 Jan 2009

4/23

Recall:

Productfacet

identification

(2 2)

Identifyfacets

manually

wouldtriggerthatfacet

>

Noise+Facetcoverage+noimplicitfacets

8/14/2019 Update - 26 Jan 2009

5/23

Recall:

Facetoriented

sentence

clustering

,

Facetis

labeled

based

on

keywords

occurrence

8/14/2019 Update - 26 Jan 2009

6/23

Infocusedsnippetsummarization

Problemformulation

(1 2)

Clustersentences

belonging

to

each

facet.

Output: Sentenceranking+grouping

Sentencepolarity

Infocused

snippet

representation

8/14/2019 Update - 26 Jan 2009

7/23

Infocusedsnippetsummarization

Problemformulation

(2 2)

Battery:

##sen1

Battery:

+sen1(and5similarities)

##sen2

##sen3

##sen4

##sen5

sen3 an 0simi arities

+sen5(and1similarities)

sen6 and2similarities

##sen6

##senN

+sen2(and2similarities)

ngroupe :

+sen7

sen10

thedrawbackswerethattheywerenotuserfriendlyforthecasualphotographer,

thelcd screenisalittletoosmall.

the

lcd screen

is

too

small

.

8/14/2019 Update - 26 Jan 2009

8/23

Infocusedsnippetsummarization:

Methodology

Editing

C ustering

Ranking

8/14/2019 Update - 26 Jan 2009

9/23

Methodology:

Editing(1 13)

Jing,H.

(2000).

Sentence

reduction

for

automatic

Machinelearningtechnique

Features:

Grammarchecking(Integratedlexicon)

Contextinformation

8/14/2019 Update - 26 Jan 2009

10/23

Methodology:

Editing(2 13)

Knight,K.,

&

Marcu,

D.

(2000).

Statistics

based

Noisychannelmodel

Themodelislearnedbymachinelearning

Syntactictree

8/14/2019 Update - 26 Jan 2009

11/23

Methodology:

Editing(3 13)

Generalpurpose

compression

targetedpartofthesentence.

8/14/2019 Update - 26 Jan 2009

12/23

Methodology:

Editing(4 13)

Initializea

focused

part

of

the

sentence

minimumyetmeaningful.

8/14/2019 Update - 26 Jan 2009

13/23

Methodology:

Editing(5 13)

the

drawbacks

were

that

they

were

not

user

friendly

for

the

casual

photographer

,

thelcd screenisalittletoosmall.

thedrawbackswerethattheywerenotuserfriendlyforthecasualphotographer,

thelcd screenisalittletoosmall.

thedrawbackswerethattheywerenotuserfriendlyforthecasualphotographer,

thelcd screenis alittletoosmall.

t e

raw ac s

were

t at

t ey

were

not

user

r en y

or

t e

casua

p otograp er

,

thelcd screenisalittletoosmall.

e c screen s oosma .

8/14/2019 Update - 26 Jan 2009

14/23

Methodology:

Editing(6 13)

Considera

sentence

can

be

represented

as

a

se uenceofwordsinthefollowin form:

NNNNNN00111000NNN

1:We

want

to

keep

the

word

at

this

position

0:Wewanttoremovethewordatthisposition

N:Wehaventdecidedwiththewordatthisposition

,wewanttokeepitornot

.

8/14/2019 Update - 26 Jan 2009

15/23

Methodology:

Editing(7 13)

N10N

N100

N101

010N

N1NN

N11N

N1N0

110N

N1N1

01NN

11N1

T=0 T=1 T=2

8/14/2019 Update - 26 Jan 2009

16/23

Methodology:

Editing(8 13)

Howlarge

is

the

search

space?

8/14/2019 Update - 26 Jan 2009

17/23

Methodology:

Editing(9 13)

Thesearchs ace:

Factorial Heuristic:Dependencylinks

Ex:Thebatterylifeofthecameraisimpressive.

det(life3,The1)

nn(life3,battery2)

,

det(camera6,

the

5)

prep_of(life3,camera6)

,

8/14/2019 Update - 26 Jan 2009

18/23

Methodology:

Editing(10 13)

Onlywords

that

has

at

least

one

link

with

the

8/14/2019 Update - 26 Jan 2009

19/23

Methodology:

Editing(11 13)

Wewant

to

compute: P(St+1|St)

where

St is

a

sequenceattimet

The Vterbi al orithm to find the maximum

sequence.

Weusethefollowin formula:

8/14/2019 Update - 26 Jan 2009

20/23

Methodology:

Editing(12 13)

movethe

state

from

tto

t+1.

LetD X =1ifXiske t otherwiseD X =0

LetEbethesetofwordsthathavebeendecidedat

timet,

and

R(X)

subset

of

E

be

the

set

of

words

that

havedependencywithX.

1+ tt

))(,()1())(,(-),(

)|)((

++=

=

XRXStatXRXgramNEXC

EXDP

1,0where

8/14/2019 Update - 26 Jan 2009

21/23

Methodology:

Editing(13 13)

=

esdependenciofsetexistingtherepresents)Re(

))Re(|),(Re(),(

E

EEXPEXC

esdependenciofsetexpandedtherepresentsE)Re(X,

WebPMIsearchProximitywithmodeledbecan))(,( + XRXgramN

evidencecorpusReview:))(,( XRXStat

8/14/2019 Update - 26 Jan 2009

22/23

measurementadapted

from

baseline

1

thresholdwilltellushowmanyclustersof

.

8/14/2019 Update - 26 Jan 2009

23/23

,

formulationof

the

editing

part

by

considering

.

Nextmeeting:

e nes ay e : am ngapore me