(C) 2000, The University of Michigan 1 Language and Information Handout #3 October 12, 2000.

(C) 2000, The University of Michigan

1

Language and Information

Handout #3

October 12, 2000


2

Course Information

• Instructor: Dragomir R. Radev ([email protected])

• Office: 305A, West Hall

• Phone: (734) 615-5225

• Office hours: TTh 3-4

• Course page: http://www.si.umich.edu/~radev/760

• Class meets on Thursdays, 5-8 PM in 311 West Hall


3

Readings

• Textbook:– Oakes, Chapter 2, pages 76-93– Oakes, Chapter 4, pages 149-150, 158-167

• Additional readings– M&S, Chapter 2, pages 60-79


4

More statistics


5

Rank correlation

• Pearson - continuous data

• Spearman’s rank correlation coefficient - non-continuous variables

= 1 - 6 d2

N (N2 - 1)


6

ExampleS X Y X' Y' d d2

1 894 80.2 2 5 3 9

2 1190 86.9 1 2 1 1

3 350 75.7 6 6 0 0

4 690 80.8 4 4 0 0

5 826 84.5 3 3 0 0

6 449 89.3 5 1 4 16

= 1 - 6 x 26

6 (62 - 1) =


7

Linear regression

• Dependent and independent variables

• Regression: used to predict the behavior of the dependent variable

• Needed: X, Y, X, b = slope of Y(X)

b = NXY - XY

NX2 - (X)2

Y’ = Y + b(X - X)


8

Example

Section X Y X2 XY

1 22 20 484 440

2 49 24 2401 1176

3 80 42 6400 3360

4 26 22 676 572

5 40 23 1600 920

6 54 26 2916 1404

7 91 55 8281 5005

TOTAL 362 212 22758 12877


9

Example (cont’d)

a = 5.775

(7 x 12877) - (362 x 212)

(7 x 22758) - (362 x 362)=

90139 - 76744

159306 - 131044=

13395

28262= 0.474b =

Y’ = 5.775 + 0.474 X


10

Text summarization


11

Some concepts

• Abstracts: “a concise summary of the central subject matter of a document” [Paice90].

• Indicative, informative, and critical summaries

• Extracts (representative sentences)


12

Informative summaries

. .

.

. . .


13

Lines sometimes blurred

Net Tax Moratorium Clears House

The House passed a bill to extend the current moratorium on new Internet taxes until 2006. The moratorium forbids states from trying to find new ways of taxing Internet use, like imposing taxes on monthly access charges for Internet service providers.


14

House Votes to Ban Internet Taxes for 5 More Years

By LIZETTE ALVAREZ

WASHINGTON, May 10 -- In a Republican bid to woo the high-technology industry and please taxpayers, the House today rushed to the floor and then handily passed a bill to extend the current moratorium on new Internet taxes until 2006.The moratorium, which is due to expire in October 2001, forbids states to try to find new ways of taxing Internet use, like imposing taxes on monthly access charges for Internet service providers.The legislation passed today, which faces an uncertain future in the Senate, does not directly address the question of sales taxes; it would not stop states from trying to collect taxes for goods sold on the Internet.By failing to address sales taxes, however, the measure alarmed some traditional retailers, as well as state governments that say they have found it nearly impossible to collect taxes for goods sold online."The single largest contributor to our economic prosperity has been the growth of information technology -- the Internet," saidRepresentative John R. Kasich, an Ohio Republican. "Why would we try to tax something, why would we try to abuse something, why would we try to limit something that generates unprecedented growth, wealth, opportunity and unprecedented individual power?"Critics of the bill say the moratorium, while seemingly benign, ignores the thorny question of how state and local governments can best collect taxes on the billions of dollars of merchandise sold over the Internet each year. These taxes are expected to provide a crucial future source of revenue for states, especially as more consumers buy goods online.The bill's opponents -- a consortium of retailers, small-business groups and governors -- say that consumers who buy merchandise over the Internet can easily circumvent the sales and "use" taxes that would be collected automatically if the same merchandise is bought at a bricks-and-mortar retail store.The National Governors' Association is working on the best way to collect electronic sales tax. Estimates have put the loss in sales tax revenue to the states at $8 billion a year by 2004.

http://www.nytimes.com/library/tech/00/05/biztech/articles/11tax.html


15

Retailers and small businesses have complained that the current system unfairly places at a disadvantage the traditional retailers that do not sell their wares online and must charge sales tax."It's easy to imagine how these kinds of losses can affect state and local governments' ability to provide essential services," said Representative William D. Delahunt, a Massachusetts Democrat, citing the concerns of many governors. "They will be compelled to cut back local services or raise income taxes or property taxes."The bill even drew criticism from a few Republicans. Representative Ernest J. Istook Jr. of Oklahoma circulated a letter stating, "The Internet should not be singled out to be taxed, nor to be freed from tax."Still, the House voted overwhelmingly, 352 to 75, to pass the bill. A number of Democrats approved the measure after they received assurance that Congress would hold hearings concerning sales taxes and would try to come up with a solution.The moratorium "has absolutely nothing to do with the sales tax -- we will have the opportunity to have that debate," said Representative Robert Goodlatte, a Virginia Republican.The House bill faces a murkier future in the Senate. Senator John McCain, chairman of the Commerce Committee, who advocates a permanent tax moratorium, canceled a hearing on the bill last month after Republican senators, some of them former governors, expressed reservations about extending the moratorium.The legislation also faces opposition from the Clinton administration, which signaled support today for a two-year moratorium. The full House today rejected a two-year extension in a separate vote.Gov. George W. Bush, the likely Republican presidential nominee, has said he will support an extension of the moratorium. But the governor must tread carefully around the issue because Texas, which does not have a state income tax, would stand to lose substantial revenue ifsales taxes are not made workable on the Internet.A spokesman for Al Gore said the vice president supported a two-year extension of the moratorium "at a minimum." If a five-year moratorium is put into place, "it should include flexibility" to adjust federal policies on Internet taxation "to take into account the fast-paced change in the Internet world.”


16

Types of summaries

• dimensions

• genres

• context


17

Dimensions

• Single-document vs. multi-document


18

Genres• headlines• outlines• minutes• biographies• abridgments• sound bites• movie summaries• chronologies, etc.

[Mani and Maybury 1999]


19

Context

• Query-specific

• Query-independent


20

What does summarization involve?

• Three stages (typically)– content identification– conceptual organization– realization


21

Spärck Jones’s three sets of factors

• Input factors (source form, subject type, unit)

• Purpose factors (situation, audience, use)

• Output factors (material, format, style)

[Spärck Jones 99]


22


23

http://transend.labs.bt.com/prosum/word/index.html

ProSum

• Profile-based summarization

• Control of summarization length

• Retention of user-defined text

• Customizable heading treatment

• Customizable table treatment

• Customizable text differentiation


24


25

Example (New York Times)

Net Tax Moratorium Clears House

The House passed a bill to extend the current moratorium on new Internet taxes until 2006.The moratorium forbids states from trying to find new ways of taxing Internet use, like imposing taxes on monthly access charges for Internet service providers.


26


By LIZETTE ALVAREZ




27



28

Microsoft Autosummarize outputHouse Votes to Ban Internet Taxes for 5 More Years

The moratorium, which is due to expire in October 2001, forbids states to try to find new ways of taxing Internet use, like imposing taxes on monthly access charges for Internet service providers.By failing to address sales taxes, however, the measure alarmed some traditional retailers, as well as state governments that say they have found it nearly impossible to collect taxes for goods sold online.

10% summary


29


By LIZETTE ALVAREZ




30



31

Microsoft Autosummarize outputHouse Votes to Ban Internet Taxes for 5 More Years

The moratorium, which is due to expire in October 2001, forbids states to try to find new ways of taxing Internet use, like imposing taxes on monthly access charges for Internet service providers.The legislation passed today, which faces an uncertain future in the Senate, does not directly address the question of sales taxes; it would not stop states from trying to collect taxes for goods sold on the Internet.By failing to address sales taxes, however, the measure alarmed some traditional retailers, as well as state governments that say they have found it nearly impossible to collect taxes for goods sold online.The National Governors' Association is working on the best way to collect electronic sales tax. Representative Ernest J. Istook Jr. of Oklahoma circulated a letter stating, "The Internet should not be singled out to be taxed, nor to be freed from tax."Senator John McCain, chairman of the Commerce Committee, who advocates a permanent tax moratorium, canceled a hearing on the bill last month after Republican senators, some of them former governors, expressed reservations about extending the moratorium.

25% summary


32


By LIZETTE ALVAREZ




33



34

OutlineIntroduction

Traditional approaches

Multi-document summarization

Knowledge-rich techniques

Evaluation

Language modeling

Conclusion + Appendix

I

II

III

IV

V

VI

VII


35

Human summarization and abstracting

• What professional abstractors do

• Ashworth:• “To take an original article, understand it and pack it

neatly into a nutshell without loss of substance or clarity presents a challenge which many have felt worth taking up for the joys of achievement alone. These are the characteristics of an art form”.


36

Borko and Bernier 75

• The abstract and its use:– Abstracts promote current awareness– Abstracts save reading time– Abstracts facilitate selection– Abstracts facilitate literature searches– Abstracts improve indexing efficiency– Abstracts aid in the preparation of reviews


37

Cremmins 82, 96

• American National Standard for Writing Abstracts:– State the purpose, methods, results, and conclusions presented in

the original document, either in that order or with an initial emphasis on results and conclusions.

– Make the abstract as informative as the nature of the document will permit, so that readers may decide, quickly and accurately, whether they need to read the entire document.

– Avoid including background information or citing the work of others in the abstract, unless the study is a replication or evaluation of their work.


38

Cremmins 82, 96

– Do not include information in the abstract that is not contained in the textual material being abstracted.

– Verify that all quantitative and qualitative information used in the abstract agrees with the information contained in the full text of the document.

– Use standard English and precise technical terms, and follow conventional grammar and punctuation rules.

– Give expanded versions of lesser known abbreviations and acronyms, and verbalize symbols that may be unfamiliar to readers of the abstract.

– Omit needless words, phrases, and sentences.


39

Cremmins 82, 96• Original version:

There were significant positive associations between the concentrations of the substance administered and mortality in rats and mice of both sexes.

There was no convincing evidence to indicate that endrin ingestion induced and of the different types of tumors which were found in the treated animals.

• Edited version:

Mortality in rats and mice of both sexes was dose related.

No treatment-related tumors were found in any of the animals.


40

Redundancy of English

• 75% redundancy of English [Shannon 51]

• [Burton & Licklider 55] show that humans are as good at guessing the next letter after seeing 32 letters as after 10,000 letters.


41

Morris et al. 92

• Reading comprehension of summaries• Compare manual abstracts, Edmundson-

style extracts, and full documents• Extracts containing 20% or 30% of original

document are effective surrogates of original document

• Performance on 20% and 30% extracts is no different than informative abstracts


42

Extraction models

• Extracts vs. abstracts• Linear model• Text structure based• New techniques

Compression Ratio =|S|

|D|

Retention Ratio =i (S)

i (D)

Information content


43

Text compaction techniquesMissam ad amicum pro onsolatione epistolam, dilectissime, vestram ad me forte quidam nuper attulit.

Quam ex ipsa statim tituli fronte vestram esse considerans, tanto ardentius eam cepi legere quanto scriptorem ipsum karius amplector, ut cuius rem perdidi verbis saltem tanquam eius quadam imagine recreer.

Erant, memini, huius epistole fere omnia felle et absintio plena, que scilicet nostre conversionis miserabilem hystoriam et tuas, unice, cruces assiduas referebant.

Complesti revera in epistola illa quod in exordio eius amico promisisti, ut videlicet in omparatione tuarum suas molestias nullas vel parvas reputaret; ubi quidem expositis prius magistrorum tuorum in te persequutionibus, deinde in corpus tuum summe proditionis iniuria, ad condiscipulorum quoque tuorum Alberici videlicet Remensis et Lotulfi Lumbardi execrabilem invidiam et infestationem nimiam stilum contulisti.

Missam ad amicum pro onsolatione epistolam, dilectissime, vestram ad me forte quidam nuper attulit.



44

Text compaction techniques

Missam ad amicum pro onsolatione epistolam, dilectissime, vestram ad me forte quidam nuper attulit.


Missam vestram nuper attulit.

Erant, scilicet nostre conversionis miserabilem hystoriam referebant.


45

Luhn 58• Very first work in

automated summarization

• Computes measures of significance

• Words:– stemming

– bag of words WORDSF

RE

QU

EN

CY

E

Resolving power of significant words


46

Luhn 58• Sentences:

– concentration of high-score words

• Cutoff values established in experiments with 100 human subjects

SIGNIFICANT WORDS

ALL WORDS

* * * * 1 2 3 4 5 6 7

SENTENCE

SCORE = 42/7 2.3


47

Edmundson 69

• Cue method:– stigma words

(“hardly”, “impossible”)

– bonus words (“significant”)

• Key method:– similar to Luhn

• Title method:– title + headings

• Location method:– sentences under

headings– sentences near

beginning or end of document and/or paragraphs (also [Baxendale 58])


48

Edmundson 69• Linear combination of

four features:

1C + 2K + 3T + 4L

• Manually labelled training corpus

• Key not important!0 10 20 30 40 50 60 70 80 90 100 %

RANDOM

KEY

TITLE

CUE

LOCATION

C + K + T + L

C + T + L

1


49

Paice 90

• Survey up to 1990• Techniques that

(mostly) failed:– syntactic criteria [Earl

70]

– indicator phrases (“The purpose of this article is to review…)

• Problems with extracts:– lack of balance

– lack of cohesion• anaphoric reference

• lexical or definite reference

• rhetorical connectives


50

Paice 90

• Lack of balance– later approaches based

on text rhetorical structure

• Lack of cohesion– recognition of

anaphors [Liddy et al. 87]

• Example: “that” is– nonanaphoric if preceded

by a research-verb (e.g., “demonstrat-”),

– nonanaphoric if followed by a pronoun, article, quantifier,…,

– external if no later than 10th word,else

– internal


51

Brandow et al. 95

• ANES: commercial news from 41 publications

• “Lead” achieves acceptability of 90% vs. 74.4% for “intelligent” summaries

• 20,997 documents• words selected based

on tf*idf• sentence-based

features:– signature words– location– anaphora words– length of abstract


52

Brandow et al. 95

• Sentences with no signature words are included if between two selected sentences

• Evaluation done at 60, 150, and 250 word length

• Non-task-driven evaluation:

“Most summaries judged less-than-perfect would not be detectable as such to a user”


53

Lin & Hovy 97

• Optimum position policy

• Measuring yield of each sentence position against keywords (signature words) from Ziff-Davis corpus

• Preferred order

[(T) (P2,S1) (P3,S1) (P2,S2) {(P4,S1) (P5,S1) (P3,S2)} {(P1,S1) (P6,S1) (P7,S1) (P1,S3)(P2,S3) …]


54

Kupiec et al. 95

• Extracts of roughly 20% of original text

• Feature set:– sentence length

• |S| > 5

– fixed phrases• 26 manually chosen

– paragraph• sentence position in

paragraph

– thematic words• binary: whether

sentence is included in manual extract

– uppercase words• not common acronyms

• Corpus:• 188 document +

summary pairs from scientific journals


55

Kupiec et al. 95

• Uses Bayesian classifier:

• Assuming statistical independence:

k

j j

k

j j

kFP

SsPSsFPFFFSsP

1

121

)(

)()|(),...,|(

),(

)()|,...,(),...,|(

,...21

2121

k

kk FFFP

SsPSsFFFPFFFSsP


56

Kupiec et al. 95

• Performance:– For 25% summaries, 84% precision– For smaller summaries, 74% improvement over

Lead


57

Salton et al. 97

• document analysis based on semantic hyperlinks (among pairs of paragraphs related by a lexical similarity significantly higher than random)

• Bushy paths (or paths connecting highly connected paragraphs) are more likely to contain information central to the topic of the article


58

Salton et al. 97

…

…


59

Salton et al. 97

Overlap between manual extracts: 46%Algorithm Optimistic Pessimistic Intersection Union

Globalbushy

45.60% 30.74% 47.33% 55.16%

Globaldepth-first

43.98% 27.76% 42.33% 52.48%

Segmentedbushy

45.48% 26.37% 38.17% 52.95%

Random 39.16% 22.07% 38.47% 44.24%


60

Marcu 97-99

• Based on RST (nucleus+satellite relations)

• text coherence• 70% precision and

recall in matching the most important units in a text

• Example: evidence

[The truth is that the pressure to smoke in junior high is greater than it will be any other time of one’s life:][we know that 3,000 teens start smoking each day.]

• N+S combination increases R’s belief in N [Mann and Thompson 88]


61

2Elaboration

2Elaboration

8Example

2BackgroundJustification

3Elaboration

8Concession

10Antithesis

Mars experiences

frigid weather

conditions(2)

Surface temperatures typically average

about -60 degrees

Celsius (-76 degrees

Fahrenheit) at the

equator and can dip to -

123 degrees C near the

poles(3)

4 5Contrast

Although the atmosphere

holds a small

amount of water, and water-ice

clouds sometimes develop,

(7)

Most Martian weather involves

blowing dust and carbon monoxide.

(8)

Each winter, for example, a blizzard of

frozen carbon dioxide

rages over one pole, and a few meters of

this dry-ice snow

accumulate as

previously frozen carbon dioxide

evaporates from the opposite

polar cap.(9)

Yet even on the summer pole, where

the sun remains in the sky all day long,

temperatures never warm

enough to melt frozen

water.(10)

With its distant orbit (50 percent farther from the sun than Earth) and

slim atmospheric

blanket,(1)

Only the midday sun at tropical latitudes is

warm enough to

thaw ice on occasion,

(4)

5Evidence

Cause

but any liquid water formed in this way would

evaporate almost

instantly(5)

because of the low

atmospheric pressure

(6)


62

Barzilay and Elhadad 97

• Lexical chains [Stairmand 96]

Mr. Kenny is the person that invented the anesthetic machine which uses micro-computers to control the rate at which an anesthetic is pumped into the blood. Such machines are nothing new. But his device uses two micro-computers to achineve much closer monitoring of the pump feeding the anesthetic into the patient.


63


• WordNet-based

• three types of relations:– extra-strong (repetitions)– strong (WordNet relations)– medium-strong (link between synsets is longer

than one + some additional constraints)


64


• Scoring chains:– Length– Homogeneity index:

= 1 - # distinct words in chain

Score = Length * Homogeneity

Score > Average + 2 * st.dev.


65

Other approaches

• Salience-based [Boguraev and Kennedy 97]

• Computational linguistics papers [Teufel and Moens 97]


66

Part III Multi-document summarization


67

Mani & Bloedorn 97,99

• Summarizing differences and similarities across documents

• Single event or a sequence of events

• Text segments are aligned

• Evaluation: TREC relevance judgments

• Significant reduction in time with no significant loss of accuracy


68

Carbonell & Goldstein 98

• Maximal Marginal Relevance (MMR)

• Query-based summaries

• Law of diminishing returns

C = doc collectionQ = user queryR = IR(C,Q,)S = already retrieved

documentsSim = similarity metric

used

MMR = argmax [ (Sim1(Di,Q) - (1-) max Sim2(Di,Dj)]DiR\S DiS


69

Radev et al. 00

• MEAD• Centroid-based• Based on sentence

utility

• Topic detection and tracking initiative [Allen et al. 98, Wayne 98]

TIME

1. Algerian newspapers have reported that 18 decapitated bodies have been found by authorities in the south of the country.

2. Police found the ``decapitated bodies of women, children and old men,with their heads thrown on a road'' near the town of Jelfa, 275 kilometers (170 miles) south of the capital Algiers.

3. In another incident on Wednesday, seven people -- including six children -- were killed by terrorists, Algerian security forces said.

4. Extremist Muslim militants were responsible for the slaughter of the seven people in the province of Medea, 120 kilometers (74 miles) south of Algiers.

5. The killers also kidnapped three girls during the same attack, authorities said, and one of the girls was found wounded on a nearby road.

6. Meanwhile, the Algerian daily Le Matin today quoted Interior Minister Abdul Malik Silal as saying that ``terrorism has not been eradicated, but the movement of the terrorists has significantly declined.''

7. Algerian violence has claimed the lives of more than 70,000 people since the army cancelled the 1992 general elections that Islamic parties were likely to win.

8. Mainstream Islamic groups, most of which are banned in the country, insist their members are not responsible for the violence against civilians.

9. Some Muslim groups have blamed the army, while others accuse ``foreign elements conspiring against Algeria.’’

1. Eighteen decapitated bodies have been found in a mass grave in northern Algeria, press reports said Thursday, adding that two shepherds were murdered earlier this week.

2. Security forces found the mass grave on Wednesday at Chbika, near Djelfa, 275 kilometers (170 miles) south of the capital.

3. It contained the bodies of people killed last year during a wedding ceremony, according to Le Quotidien Liberte.

4. The victims included women, children and old men.

5. Most of them had been decapitated and their heads thrown on a road, reported the Es Sahafa.

6. Another mass grave containing the bodies of around 10 people was discovered recently near Algiers, in the Eucalyptus district.

7. The two shepherds were killed Monday evening by a group of nine armed Islamists near the Moulay Slissen forest.

8. After being injured in a hail of automatic weapons fire, the pair were finished off with machete blows before being decapitated, Le Quotidien d'Oran reported.

9. Seven people, six of them children, were killed and two injured Wednesday by armed Islamists near Medea, 120 kilometers (75 miles) south of Algiers, security forces said.

10. The same day a parcel bomb explosion injured 17 people in Algiers itself.

11. Since early March, violence linked to armed Islamists has claimed more than 500 lives, according to press tallies.

ARTICLE 18854: ALGIERS, May 20 (UPI) ARTICLE 18853: ALGIERS, May 20 (AFP)


71

Vector-based representationTerm 1

Term 2

Term 3

Document

Centroid


72

Vector-based matching

• The cosine measure

kk

kk

k kk

cd

kidfcdCDsim

22 .

)(..),(


73

CIDR

sim T

sim < T


74

CentroidsC 00022 (N=44)

(10000)diana 1.93princess 1.52

C 00025 (N=19)(10000)albanians 3.00

C 00026 (N=10)(10000)universe 1.50

expansion 1.00bang 0.90

C 10007 (N=11)(10000)crashes 1.00

safety 0.55transportat

ion0.55

drivers 0.45board 0.36flight 0.27buckle 0.27

pittsburgh 0.18graduating 0.18automobile 0.18

C 00035 (N=22)(10000)airlines 1.45

finnair 0.45

C 00031 (N=34)(10000)el 1.85

nino 1.56

C 00008 (N=113)(10000)space 1.98

shuttle 1.17station 0.75nasa 0.51

columbia 0.37mission 0.33mir 0.30

astronauts

0.14steering 0.11safely 0.07

C 10062 (N=161)microsoft 3.24justice 0.93

department

0.88windows 0.98corp 0.61

software 0.57ellison 0.07hatch 0.06

netscape 0.04metcalfe 0.02


75

MEAD

...

...


76

MEAD

• INPUT: Cluster of d documents with n sentences (compression rate = r)

• OUTPUT: (n * r) sentences from the cluster with the highest values of SCORE

SCORE (s) = i (wcCi + wpPi + wfFi)


77

[Barzilay et al. 99]

• Theme intersection (paraphrases)

• Identifying common phrases across multiple sentences:– evaluated on 39 sentence-level predicate-

argument structures– 74% of p-a structures automatically identified


78

Other multi-document approaches

• Reformulation [McKeown et al. 99]

• Generation by Selection and Repair [DiMarco et al. 97]

• Topic and event distinctions [Fukumoto & Suzuki 00]


79

Overview• Schank and Abelson 77

– scripts

• DeJong 79– FRUMP (slot-filling from UPI news)

• Graesser 81– Ratio of inferred propositions to these explicitly

stated is 8:1

• Young & Hayes 85– banking telexes


80

Radev and McKeown 98MESSAGE: ID TST3-MUC4-0010 MESSAGE: TEMPLATE 2 INCIDENT: DATE 30 OCT 89 INCIDENT: LOCATION EL SALVADOR INCIDENT: TYPE ATTACK INCIDENT: STAGE OF EXECUTION ACCOMPLISHED INCIDENT: INSTRUMENT ID INCIDENT: INSTRUMENT TYPEPERP: INCIDENT CATEGORY TERRORIST ACT PERP: INDIVIDUAL ID "TERRORIST" PERP: ORGANIZATION ID "THE FMLN" PERP: ORG. CONFIDENCE REPORTED: "THE FMLN" PHYS TGT: ID PHYS TGT: TYPEPHYS TGT: NUMBERPHYS TGT: FOREIGN NATIONPHYS TGT: EFFECT OF INCIDENTPHYS TGT: TOTAL NUMBERHUM TGT: NAMEHUM TGT: DESCRIPTION "1 CIVILIAN"HUM TGT: TYPE CIVILIAN: "1 CIVILIAN"HUM TGT: NUMBER 1: "1 CIVILIAN"HUM TGT: FOREIGN NATIONHUM TGT: EFFECT OF INCIDENT DEATH: "1 CIVILIAN"HUM TGT: TOTAL NUMBER


81

Generating text from templates

On October 30, 1989, one civilian was killed in a reported FMLN attack in El Salvador.


82

Input: Cluster of templates

T1 Tm

Conceptual combiner

T2 …..

Combiner

Paragraph planner

Planningoperators

Linguistic realizer

Sentence planner

Sentence generator

Lexical chooserLexicon

OUTPUT: Base summary

SURGE

Domainontology


83

Excerpts from four articles JERUSALEM - A Muslim suicide bomber blew apart 18 people on a Jerusalem bus and wounded 10 in a mirror-image of an attack one week ago. The carnage could rob Israel's Prime Minister Shimon Peres of the May 29 election victory he needs to pursue Middle East peacemaking. Peres declared all-out war on Hamas but his tough talk did little to impress stunned residents of Jerusalem who said the election would turn on the issue of personal security.

JERUSALEM - A bomb at a busy Tel Aviv shopping mall killed at least 10 people and wounded 30, Israel radio said quoting police. Army radio said the blast was apparently caused by a suicide bomber. Police said there were many wounded.

A bomb blast ripped through the commercial heart of Tel Aviv Monday, killing at least 13 people and wounding more than 100. Israeli police say an Islamic suicide bomber blew himself up outside a crowded shopping mall. It was the fourth deadly bombing in Israel in nine days. The Islamic fundamentalist group Hamas claimed responsibility for the attacks, which have killed at least 54 people. Hamas is intent on stopping the Middle East peace process. President Clinton joined the voices of international condemnation after the latest attack. He said the ``forces of terror shall not triumph'' over peacemaking efforts.

TEL AVIV (Reuter) - A Muslim suicide bomber killed at least 12 people and wounded 105, including children, outside a crowded Tel Aviv shopping mall Monday, police said. Sunday, a Hamas suicide bomber killed 18 people on a Jerusalem bus. Hamas has now killed at least 56 people in four attacks in nine days. The windows of stores lining both sides of Dizengoff Street were shattered, the charred skeletons of cars lay in the street, the sidewalks were strewn with blood. The last attack on Dizengoff was in October 1994 when a Hamas suicide bomber killed 22 people on a bus.

1

2

3

4


84

Four templates

MESSAGE: ID TST-REU-0001 SECSOURCE: SOURCE Reuters SECSOURCE: DATE March 3, 1996 11:30 PRIMSOURCE: SOURCE INCIDENT: DATE March 3, 1996 INCIDENT: LOCATION Jerusalem INCIDENT: TYPE Bombing HUM TGT: NUMBER “killed: 18'' “wounded: 10” PERP: ORGANIZATION ID

MESSAGE: ID TST-REU-0002 SECSOURCE: SOURCE Reuters SECSOURCE: DATE March 4, 1996 07:20 PRIMSOURCE: SOURCE Israel Radio INCIDENT: DATE March 4, 1996 INCIDENT: LOCATION Tel Aviv INCIDENT: TYPE Bombing HUM TGT: NUMBER “killed: at least 10'' “wounded: more than 100” PERP: ORGANIZATION ID

MESSAGE: ID TST-REU-0003 SECSOURCE: SOURCE Reuters SECSOURCE: DATE March 4, 1996 14:20 PRIMSOURCE: SOURCE INCIDENT: DATE March 4, 1996 INCIDENT: LOCATION Tel Aviv INCIDENT: TYPE Bombing HUM TGT: NUMBER “killed: at least 13'' “wounded: more than 100” PERP: ORGANIZATION ID “Hamas”

MESSAGE: ID TST-REU-0004 SECSOURCE: SOURCE Reuters SECSOURCE: DATE March 4, 1996 14:30 PRIMSOURCE: SOURCE INCIDENT: DATE March 4, 1996 INCIDENT: LOCATION Tel Aviv INCIDENT: TYPE Bombing HUM TGT: NUMBER “killed: at least 12'' “wounded: 105” PERP: ORGANIZATION ID

43

21


85

Fluent summary with comparisons

Reuters reported that 18 people were killed on Sunday in a bombing in Jerusalem. The next day, a bomb in Tel Aviv killed at least 10 people and wounded 30 according to Israel radio. Reuters reported that at least 12 people were killed and 105 wounded in the second incident. Later the same day, Reuters reported that Hamas has claimed responsibility for the act.

(OUTPUT OF SUMMONS)


86

Operators• If there are two templates

ANDthe location is the same

ANDthe time of the second template is after the time of the first template

ANDthe source of the first template is different from the source of the second template

ANDat least one slot differs

THENcombine the templates using the contradiction operator...


87

Operators: Change of Perspective

Change of perspective

March 4th, Reuters reported that a bomb in Tel Aviv killed at least 10 people and wounded 30. Later the same day, Reuters reported that exactly 12 people were actually killed and 105 wounded.

Precondition:The same source reports a change in a small number of slots


88

Operators: ContradictionContradiction

The afternoon of February 26, 1993, Reuters reported that a suspected bomb killed at least six people in the World Trade Center. However, Associated Press announced that exactly five people were killed in the blast.

Precondition:Different sources report contradictory values for a small number of slots


89

Operators: Refinement and Agreement

Refinement

On Monday morning, Reuters announced that a suicide bomber killed at least 10 people in Tel Aviv. In the afternoon, Reuters reported that Hamas claimed responsibility for the act.

Agreement

The morning of March 1st 1994, both UPI and Reuters reported that a man was kidnapped in the Bronx.


90

Operators: GeneralizationGeneralization

According to UPI, three terrorists were arrested in Medellín last Tuesday. Reuters announced that the police arrested two drug traffickers in Bogotá the next day.

A total of five criminals were arrested in Colombia last week.


91

Other conceptual methods

• Operator-based transformations using terminological knowledge representation [Reimer and Hahn 97]

• Topic interpretation [Hovy and Lin 98]


92

Overview of techniques

• Extrinsic techniques (task-based)

• Intrinsic techniques


93

• Can you recreate what’s in the original? – the Shannon Game [Shannon 1947–50].

– but often only some of it is really important. • Measure info retention (number of keystrokes):

– 3 groups of subjects, each must recreate text:• group 1 sees original text before starting. • group 2 sees summary of original text before starting. • group 3 sees nothing before starting.

• Results (# of keystrokes; two different paragraphs):

Group 1 Group 2 Group 3approx. 10 approx. 150 approx. 1100

Hovy 98


94

• Burning questions:1. How do different evaluation methods compare for each type of summary? 2. How do different summary types fare under different methods? 3. How much does the evaluator affect things?

4. Is there a preferred evaluation method?

Shannon Q&A

Original 1 1 1 1 1

Abstract Background 1 3 1 1 1Just-the-News 3 1 1 1

Regular 1 2 1 1 1Extract Keywords 2 4 1 1 1

Random 3 1 1 1

No Text 3 5

1-2: 50% 1-2: 30%2-3: 50% 2-3: 20%

3-4: 20%4-5:100%

Classification

Hovy 98

• Small Experiment– 2 texts, 7 groups.

• Results:– No difference!– As other

experiment…– ? Extract is best?


95

Precision and Recall

Relevant Non-relevant

System:relevant

A BSystem:

non-relevantC D


96

Precision and Recall

CA

A R

:Recall

BA

A P

:Precision

)(

2

RP

PRF


97

Jing et al. 98

• Small experiment with 40 articles

• When summary length is given, humans are pretty consistent in selecting the same sentences

• Percent agreement

• Different systems achieved maximum performance at different summary lengths

• Human agreement higher for longer summaries


98

SUMMAC [Mani et al. 98]

• 16 participants• 3 tasks:

– ad hoc: indicative, user-focused summaries

– categorization: generic summaries, five categories

– question-answering

• 20 TREC topics• 50 documents per

topic (short ones are omitted)


99

SUMMAC [Mani et al. 98]

• Participants submit a fixed-length summary limited to 10% and a “best” summary, not limited in length.

• variable-length summaries are as accurate as full text

• over 80% of summaries are intelligible

• technologies perform similarly


100

Goldstein et al. 99

• Reuters, LA Times• Manual summaries• Summary length rather

than summarization ratio is typically fixed

• Normalized version of R & F.

C)B,A(A

A R'

min

)R(P

PR F

'

''

2


101

Goldstein et al. 99

b)(

bp p'

1

)(

b)(g

gs

g

gs

)()(

'

''

• How to measure relative performance?

p = performanceb = baselineg = “good” systems = “superior” system


102

Radev et al. 00

---S10

---S9

---S8

---S7

---S6

---S5

+--S4

---S3

+++S2

-++S1

System 2System 1Ideal

Cluster-Based Sentence Utility


103

Cluster-Based Sentence Utility

---S10

---S9

---S8

---S7

---S6

---S5

+--S4

---S3

+++S2

-++S1


9(+)67S4

432S3

8(+)9(+)8(+)S2

510(+)10(+)S1


Summary sentence extraction method

CBSU method

CBSU(system, ideal)= % of ideal utility covered by system summary


104

Interjudge agreement

Judge1 Judge2 Judge3

Sentence 1 10 10 5

Sentence 2 8 9 8

Sentence 3 2 3 4

Sentence 4 5 6 9


105

Relative utility


Sentence 1 10 10 5

Sentence 2 8 9 8

Sentence 3 2 3 4

Sentence 4 5 6 9

RU =


106

Relative utility


Sentence 1 10 10 5

Sentence 2 8 9 8

Sentence 3 2 3 4

Sentence 4 5 6 9

17

RU =


107

Relative utility


Sentence 1 10 10 5

Sentence 2 8 9 8

Sentence 3 2 3 4

Sentence 4 5 6 9

1317

RU =

= 0.765


108

Normalized System Performance

1.000

0.765

0.765

Judge 3

0.7560.7890.722Judge 3

0.8831.0001.000Judge 2

0.8831.0001.000Judge 1

AverageJudge 2Judge 1

D = (S-R)

(J-R)

System performance

Interjudge agreement

Normalized system performance Random performance


109

Random Performance

D = (S-R)

(J-R)


110

Random Performance

D = (S-R)

(J-R)

n !

( n(1-r))! (r*n)!systemsaverage of all


111

Random Performance

D = (S-R)

(J-R)

n !

( n(1-r))! (r*n)!systemsaverage of all

{12}{13}{14}{23}{24}{34}


112

Examples

0.833 - 0.732

0.841 - 0.732= 0.927D {14} =

(S-R)

(J-R)=


113

Examples

0.833 - 0.732

0.841 - 0.732= 0.927D {14} =

(S-R)

(J-R)=

0.963D {24} =


114

1.0

J = 0.841

0.5

0.0

J’ = 1.0

0.5

R’= 0.0

R = 0.732

S = 0.833

S’ = 0.927 = D

Normalized evaluation of {14}


115

Cross-sentence Informational Subsumption and Equivalence

• Subsumption: If the information content of sentence a (denoted as I(a)) is contained within sentence b, then a becomes informationally redundant and the content of b is said to subsume that of a:

I(a) I(b)

• Equivalence: If I(a) I(b) I(b) I(a)


116

Example

(1) John Doe was found guilty of the murder.

(2) The court found John Doe guilty of the murder of Jane Doe last August and sentenced him to life.


117

Cross-sentence Informational Subsumption

967S4

432S3

898S2

51010S1

Article 3Article 2Article 1


118

Toxic spill in SpainAP, NYTTDT-3 corpus, topic 67833F

General strike in Denmark

AP, PRI, VOATDT-3 corpus, topic 7815110E

Explosion in a Moscow apartment building (Sept. 13, 1999)

AP, AFP, UPIclari.world.europe.russia1897D

Explosion in a Moscow apartment building (Sept. 9, 1999)AP, AFPclari.world.europe.russia652C

The FBI puts Osama bin Laden on the most wanted listAFP, UPIclari.world.terrorism453B

Algerian terrorists threaten BelgiumAFP, UPIclari.world.africa.northwestern252A

topicnews sourcessource# sents# docsCluster

Evaluation


119

0.75

0.8

0.85

0.9

0.95

1

10 20 30 40 50 60 70 80 90 100

Compression rate (r)

Ag

ree

me

nt

(J) Cluster A

Cluster B

Cluster C

Cluster D

Cluster E

Cluster F

Inter-judge agreementversus compression


120

4A2-8----A1-7

4A2-7----A1-6

2A2-4A2-2-A2-1-A1-5

4A2-10-A2-10A2-10A2-10A1-4

4A2-10----A1-3

3A2-5--A2-5A2-5A1-2

3A2-1-A2-1A2-1-A1-1

- score+ scoreJudge5Judge4Judge3Judge2Judge1Sent

Evaluating Sentence Subsumption


121

Subsumption (Cont’d)

SCORE (s) = i (wcCi + wpPi + wfFi) - wRRs

Rs = cross-sentence word overlap

Rs = 2 * (# overlapping words) / (# words in sentence 1 + # words in sentence 2)

wR = Maxs (SCORE(s))


122

Subsumption analysis

0107070112112

7323520284454633

11035837910163614

610731880450240705

-+-+-+-+-+-+#judges agreeing

Cluster FCluster ECluster DCluster CCluster BCluster A

Total: 558 sentences, full agreement on 292 (1+291), partial on 406 (23+383)Of 80 sentences with some indication of subsumption, only 24 had agreement of 4 or more judges.


123

Results10% 20% 30% 40% 50% 60% 70% 80% 90%

Cluster A 0.855 0.572 0.427 0.759 0.862 0.910 0.554 1.001 0.584

Cluster B 0.365 0.402 0.690 0.714 0.867 0.640 0.845 0.713 1.317

Cluster C 0.753 0.938 0.841 1.029 0.751 0.819 0.595 0.611 0.683

Cluster D 0.739 0.764 0.683 0.723 0.614 0.568 0.668 0.719 1.100

Cluster E 1.083 0.937 0.581 0.373 0.438 0.369 0.429 0.487 0.261

Cluster F 1.064 0.893 0.928 1.000 0.732 0.805 0.910 0.689 0.199

MEAD performed better than Lead in 29 (in bold) out of 54 cases.

MEAD+Lead performed better than the Lead baseline in 41 cases


124

Donaway et al. 00

• Sentence-rank based measures– IDEAL={2,3,5}:

compare {2,3,4} and {2,3,9}

• Content-based measures– vector comparisons of summary and document


125

Proposed TIDES evaluation

• Creation of corpora

• Development of evaluation software

• TREC-style evaluation

• Intrinsic and extrinsic evaluations

• Multilingual summaries (over time)

• Question-answering evaluation


126

Language modeling

• Source/target language• Coding process

Noisy channel Recovery

e f e*


128

Summarization using LM

• Source language: full document• Target language: summary


129

Berger & Mittal 00

• Gisting (OCELOT)

• content selection (preserve frequencies)• word ordering (single words, consecutive

positions)• search: readability & fidelity

g* = argmax p(g|d) = argmax p(g) . p(d|g)g g


130

Berger & Mittal 00

• Limit on top 65K words• word relatedness = alignment• Training on 100K summary+document pairs• Testing on 1046 pairs• Use Viterbi-type search• Evaluation: word overlap (0.2-0.4)• transilingual gisting is possible• No word ordering


131

Berger & Mittal 00

Sample output:

Audubon society atlanta area savannah georgia chatham and local birding savannah keepers chapter of the audubon georgia and leasing


132

Banko et al. 00

• Summaries shorter than 1 sentence• headline generation• zero-level model: unigram probabilities• other models: Part-of-speech and position• Sample output:

Clinton to meet Netanyahu Arafat Israel


133

Knight and Marcu 00

• Use structured (syntactic) information

• Two approaches:– noisy channel– decision based

• Longer summaries

• Higher accuracy


134

Some observations

• Summarization is coming of age

• For general domains: sentence extraction

• IR techniques not always appropriate: NLP needed

• New challenges: language modeling, multilingual summaries


135

Conferences

• Dagstuhl Meeting, 1993 (Karen Spärck Jones, Brigitte Endres-Niggemeyer)

• ACL/EACL Workshop, Madrid, 1997 (Inderjeet Mani, Mark Maybury)

• AAAI Spring Symposium, Stanford, 1998 (Dragomir Radev, Eduard Hovy)

• ANLP/NAACL, Seattle, 2000 (Udo Hahn, Chin-Yew Lin, Inderjeet Mani, Dragomir Radev)

• NAACL, Pittsburgh (planned), 2001


136

Readings

http://mitpress.mit.edu/book-table-of-contents.tcl?isbn=0262133598

(A detailed bibliography is available at the end of this handout)

Advances in Automatic Text Summarization by Inderjeet Mani and Mark T. Maybury (eds.)


137

1 Automatic Summarizing : Factors and Directions (K. Spärck-Jones )

2 The Automatic Creation of Literature Abstracts (H. P. Luhn)

3 New Methods in Automatic Extracting (H. P. Edmundson)

4 Automatic Abstracting Research at Chemical Abstracts Service (J. J. Pollock and A. Zamora)

5 A Trainable Document Summarizer (J. Kupiec, J. Pedersen, and F. Chen)

6 Development and Evaluation of a Statistically Based Document Summarization System (S. H. Myaeng and D. Jang)

7 A Trainable Summarizer with Knowledge Acquired from Robust NLP Techniques (C. Aone, M. E. Okurowski, J. Gorlinsky, and B. Larsen)

8 Automated Text Summarization in SUMMARIST (E. Hovy and C. Lin)

9 Salience-based Content Characterization of Text Documents (B. Boguraev and C. Kennedy)

10 Using Lexical Chains for Text Summarization (R. Barzilay and M. Elhadad)

11 Discourse Trees Are Good Indicators of Importance in Text (D. Marcu)

12 A Robust Practical Text Summarizer (T. Strzalkowski, G. Stein, J. Wang, and B. Wise)

13 Argumentative Classification of Extracted Sentenses as a First Step Towards Flexible Abstracting (S. Teufel and M. Moens)

14 Plot Units: A Narrative Summarization Strategy (W. G. Lehnert)

15 Knowledge-based text Summarization: Salience and Generalization Operators for Knowledge Base Abstraction (U. Hahn and U. Reimer)

16 Generating Concise Natural Language Summaries (K. McKeown, J. Robin, and K. Kukich)

17 Generating Summaries from Event Data (M. Maybury)

18 The Formation of Abstracts by the Selection of Sentences (G. J. Rath, A. Resnick, and T. R. Savage)

19 Automatic Condensation of Electronic Publications by Sentence Selection (R. Brandow, K. Mitze, and L. F. Rau)

20 The Effects and Limitations of Automated Text Condensing on Reading Comprehension Performance (A. H. Morris, G. M. Kasper, and D. A. Adams)

21 An Evaluation of Automatic Text Summarization Systems (T. Firmin and M J. Chrzanowski)

22 Automatic Text Structuring and Summarization (G. Salton, A. Singhal, M. Mitra, and C. Buckley)

23 Summarizing Similarities and Differences among Related Documents (I. Mani and E. Bloedorn)

24 Generating Summaries of Multiple News Articles (K. McKeown and D. R. Radev)

25 An Empirical Study of the Optimal Presentation of Multimedia Summaries of Broadcast News (A Merlino and M. Maybury)

26 Summarization of Diagrams in Documents (R. P. Futrelle)


138

Collections of papers

• Information Processing and Management, 1995

• Computational Linguistics (in progress), 2001


139

Web resources

http://www.si.umich.edu/~radev/summarizationhttp://www.cs.columbia.edu/~jing/summarization.htmlhttp://www.dcs.shef.ac.uk/~gael/alphalist.html http://www.csi.uottawa.ca/tanka/ts.html


140

Ongoing projects

• Columbia

• ISI

• CMU, JPRC, etc.

• Michigan

• elsewhere ...


141

Existing companies/systems

• Microsoft

• British Telecom

• http://extractor.iit.nrc.ca/

• inXight

• http://www.islandsoft.com/products.html (IslandInTEXT )


142

Available corpora– SUMMAC corpus

• send mail to [email protected]

– <Text+Abstract+Extract> corpus• send mail to [email protected]

– Open directory project• http://dmoz.org

– MDS corpus• send mail to [email protected]


143

Possible research topics

• Corpus creation and annotation

• MMM: Multidocument, Multimedia, Multilingual

• Evolving summaries

• Personalized summarization

• Web-based summarization


144

Number Relationship type Level Description1 Identity Any The same text appears in more than one

location2 Equivalence (paraphrasing) S, D Two text spans have the same

information content3 Translation P, S Same information content in different

languages4 Subsumption S, D One sentence contains more

information than another5 Contradiction S, D Conflicting information6 Historical background S Information that puts current

information in context7 Cross-reference P The same entity is mentioned8 Citation S, D One sentence cites another document9 Modality S Qualified version of a sentence10 Attribution S One sentence repeats the information of

another while adding an attribution11 Summary S, D Similar to Summary in RST: one

sentence summarizes another

Cross-document structure theory


145

DOC 1

Word levelPhrase level Paragraph/sentence levelDocument level

DOC 2 DOC 3

phrasal link

word link

cross-sentential link

cross-document link


146

1. Clustering 2. DocumentAnalysis

3. LinkAnalysis

4. Summarization


147

Principles of Summarization

• Put a disclaimer indicating that (automated) summaries may not preserve the emphasis and meaning of the document.

• Preserve attribution.

• Always give users a pointer to the original document.

• Indicate that the summary has been generated automatically.

• In case of conflicting sources, give all points of view.


148

Bibliography


149

THE END

(C) 2000, The University of Michigan 1 Language and Information Handout #3 October 12, 2000.

Documents

Transcript of (C) 2000, The University of Michigan 1 Language and Information Handout #3 October 12, 2000.