DEIM Forum 2014 A1-3

Edit summarization in Wikipedia based on supergram selection
呉

建民† 李

波南‡ 岩井原瑞穂※†早稲田大学大学院情報生産システム研究科〒808-0135 福岡県北九州市若松区ひびきの ２－７
E-mail: †jianmin.wu@moegi.waseda.jp,‡libonan@ruri.waseda.jp※iwaihara@waseda.jp

Abstract Document summarization has been well studies in recent years, but the basis of the existing methods fails in the
scenario of Wikipedia edit history, in which revisions have significant mutual overlaps. In this paper, we propose a method toautomatically summarize contributed contents during a specified edit period of a Wikipedia article, into a group of
maximal-length phrases, which we call supergrams. Two supergram selection algorithms, TF-IDF and Extended LDA ranking
are developed to pick up representative supergrams. We conduct a preliminary objective evaluation on these methods'
capabilities of summarizing on short text fragments against conventional document summarization methods.
Keyword Wikipedia, text summarization, text mining
initial revision to the current revision. When a new

1. Introduction
User-generated contents (UGCs) [2] are contributed

revision is edited not from the current revision but from avoluntarily by ordinary people and distributed throughpast revision, all the revisions that do not have a directedinteractive medias such as blogs, discussion form posts,

path to the new revision will belong to one of thewikis, and digital images. The use of UGCs has seen rapid

branches.

growth in recent years. Wikipedia [ 18] is among the mostsuccessful UGC platforms, known as the largest online
encyclopedia, in which articles are constantly contributedand edited by users. The collaborative online encyclopedia
currently ranks 6th on the Alexa list of top web
destinations [16].
Past revisions of Wikipedia articles after edits areaccessible from the public for confirming the edit process.
The edit history of one article can be accessed by clicking
the "history" tab at the top of the page [19]. The pagehistory contains a list of the page's previous revisi ons,
including the date and time of each edit, the username or
IP address of the user who authored it, and their edit
summary. A revision graph is a DAG (directed acyclic
graph) where each node represents one revision and eachFig. 1. Example of a reconstructed revision graph (tree)

directed edge represents a derivation relationship from theorigin node to the destination node. Users create a new

One of major issues in utilizing Wikipedia edit history

revision by editing either the current revision, or one ofis that the degree of similarity between consecutive

past revisions. Also, a completely new input may replacerevisions is very high. In the revision graph, each edge <v 1 ,the current revision. In general, confluence of edges

v 2 > corresponds to the edit from revision r 1 to revision r 2 .

occurs when one revision is created by merging multiple

By taking document difference (or diff) between v 1 and v 2 ,parents.

what changes were made between them can be obtained.

But in real edit histories, such merges are

seldom observed.

Therefore, we focus on edit history

Figure 2 shows Jaccard similarity of two adjacentthat is modeled as a revision tree, as shown in Figure 1.

revisions of article “ Natal Chart,” where the average ofIn this paper, we call by a branch a subtree that issimilarity is 98.2%, while we can observe significant

generated by removing the unique directed path from thechanges through occasional drops of similarity.

revisions, such as significant overlaps and minor changes,
are quite different from scientific documents or news
articles.
To address the challenges mentioned above, in thispaper, we focus on the following three issues:
1. Detect each change of revisions of a given article byunigrams.
2.

Constructutilizing
Fig. 2. Jaccard similarity between two adjacent revisions ofeasilysupergrams

understandable
[14],

which

unchanged token sequences.

summaries
arebyconsecutive

We consider generating

summaries for each meaningful part of the revision graph,article“ Natal Chart”

such as a scope in the mainstream and extinct branches.
Table 1 shows branch statistics of randomly-chosen 10

3. Capture topic keywords from supergrams, but if

articles from Wikipedia, in which a number of branches insupergrams are too many or too long, rank keywords by

the revision graph is detected by our algorithm[14]. The

TF-IDF. Also mark up the revision graph of Wikipedia

causes of branches can be classified into a number ofwith generated summaries.

reasons, such as: revert of a minor update, malicious

The rest of this paper is organized as follows. In Section 2

editing, paragraph reorganization, and topic removal. For

we describe the background of this research and survey

validating edit process, it is necessary to summarize

related work. In Section 3 we describe basic concepts

changes occurred in these branches. Meanwhile, a delta, orregarding our problem, and explain our method to

diff

for

construct supergrams and generate summaries. In Section

summarization, because it is too large, too much detailed,

4 we show experimental evaluation of our method and the

too little, or too much fragmented . So our objective is toresults. Finally, we conclude the paper with future work infind summarization of deltas that are reflecting edit

Section 5.

betweenrevisions,

is

often

inappropriate

history well and easily understood by human.
Table 1.
ID

Article Title

1

Racism2

2006

2. Related work

Wikipedia articles statistics.

Israel – Gaza

Wikipedia is a representative example of web sites

Total #

# ofdelivering UGCs. In Wikipedia, users other than theof revisions

Branches

contributor of an article may evaluate the article, suggest

10,89623

changes, or even make changes [12]. A warning system in

2,456

12

Wikipedia is in operation such that a warning is given
when an author is just espousing an opinion, certain

conflict
3

PhpBB

1,312

37

4

Edith Wharton

1,114

16

5

Federal republic

717

33

6

Sarkar Raj

592

15

7

Grade inflation

456

24

8

Natal chart

346

11

9

Muhammad Naguib

283

8

10

Clarinet Concerto

256

12

Topic evolution in a series of scientific documents canreveal how research on one topic influenced research on
another and helps us understand the lineage of topics [5].
We may adopt topic tracking to revision history of
Wikipedia by edit summarization, to understand andobjectively evaluate the contribution of an editor or anarticle.

However,thecharacteristics

ofWikipedia

statements are not verifiable, or has been called into
question by other users.
Regarding topic detection, Latent Dirichlet Allocation
(LDA)

[1]

is

proposedasaflexible

generative

probabilistic model for collections of discrete data. The
basic idea of LDA is that a document can be considered asa mixture of a limited number of topics and eachmeaningful word in the document can b e associated with
one of these topics. But this algorithm is suitable fordetecting prevalent topics for a large corpus consisting ofdifferent articles. For selecting significant phrases fromoverlapping deltas, we need a different mechanism.
Zhu et al. [15] proposed an algorithm to accomplish
topic detection and tracking task (TDT) in collaborativeenvironments of threaded discussions. In our problem,changes in creating a new revision of a Wikipedia article

occur in various scales and styles. When the si ze of aconstructed similarly. But since articles are generally

change is small, we cannot detect topics just from one

growing in size over time, we focus on addition deltas.

delta.

3.2

Scoping mainstream

TIARA-generated visual text summary [ 7][9] is to build

We extract revisions of the target article from thea topic-based, interactive visual analytic tool that aids

Wikipedia edit history file, where the revisions areuser in analyzing large collections of text. But it is based

numbered in chronological order[20]. Then we performon parallel topics and each topic must exist in the entire

preprocessing,

lifespan.

markup language operators, and vand alism [8]. A simple

Yan Chen et al. [3] proposed a real-time framework forversion of vandalism is detected as deletion of the wholedetecting hot emerging topics for organizations in social

article.

media context. Developed semi-supervised learners to

such

as

removing

punctuations,wiki

We extract the revision graph consisting of vertices andfacilitate timely identification of hot emerging topics foredges,

organizations. But their styles of how new entries are

mainstream is the path from the initial revision to thecreated are quite different from revision history. Kittur etcurrent revision. Each branch is regarded as a subtree

al. [6] studied the distribution of topics over two years ofrooted at a revision vertex of the mainstream. The

edit history in Wikipedia. However, their work foc uses on

mainstream can have an extremely larger tree height than

the global topic trend of all articles' edit history rather

branches. Assigning a single topic to such a long path isthan a single one. Also the topic of each article isinsufficient for capturing topic changes within the path.

calculated from its annotated Wikipedia categories, while

So we need to find significant points that indicate a topic

our work is focused on revision -wise topic changes.

change, and divide the mainstream into scopes. We also

Georgescu et al[4]. studied temporal summarization ofassume that revisions in a branch form a scope. We check

Wikipedia

burststhe changes of the text length (token counts) of each

responding to external events. However, their approachrevision along the timeline. When the size of the diff ofdoes not reflect edit contexts. We utilize revision graphs

two adjacent revisions is greater than a certain threshold,to detect edit contexts and reflect into summarizations,

we regard it as the starting point of a new topic. Here wesuch as whether revision deltas are in an extinct branch or

set the threshold as 50 tokens.

updatesthroughdetectingedit

surviving.

thenobtainmainstream

andbranches.

The

3.3 Challenge of summarizing deltas
Let us consider a simple idea of adding portions ofdeltas to the revision graph, to recognize trends of edits.

3. Edit summarization on revision graph

However, simply labelling graph edges by deltas produces
3.1

Revision graph and delta

floods of text, or hard-to-read text fragments. In this case,A revision graph G is a directed graph where each edge
representsthederivation

relationshipbetweentworevisions. From a revision graph we can detect edit reverts
with scale in terms of branch sizes.

However, without

textual description of deltas, it is difficult to analyze thecauses of branches, such as addition of new topics,
rephrasing of existing topics, or vandalisms. It is also
important to know the time when a particular topic is
introduced to an Wikipedia article. In order to detect topic
evolution along the revision graph, we collect revisiondeltas from each of the two adjacent revisions first.
Definition 1 (Delta) Given an edge <v i ,v j > in a revisiongraph G, the delta D of <v i ,v j > is the sequence <t 1 , f 1 >, <t 2 ,we need to find appropriate summarization of deltas , suchas:
1. Phrases that capture topics of deltas should be
extracted. Deltas are diverse in size; larger deltas cancontain a complete sentence or a paragraph, but smallerdeltas are insufficient to find phrases. In this case, weneed to extract text surrounding the small delta.
2. Minor updates, such as capitalizing/uncapitalizing
letters, spell correction, plural transformation, should be
ignored.
3. One delta may consist of multiple text fragments,
interrupted by wiki markups, URL links, nonsense words,
and identifiers. We need to filter out them by a stopword

f 2 >,..., <t n , f n > such that t k is an added token between v ilist and regular expressions. Only meaningful fragments

and v j , and f k is the frequency of token t k in revision v j .

need to be detected.

Note that the above deltas are b ased on addition topreceding revisions. Deltas based on deletion frompreceding revisions, and topic detection from them can be

4. To avoid flooding, we should not to adorn every edge
with summaries. Only significant edges need to beadorned.
3.4

such

unchanged

consecutive

token

sequencesinto

supergrams.

Token transition graph construction

Now we discuss extracting phrases from deltas, where

DEFINITION 2 (Supergram) A supergram s=t 1 t 2 t 3 ...t ndeltas are taken from a branch or a scope in thein a delta set DS is an n-gram (n>=1) such that s occurs inmainstream, to reflect the structure of the revision graph.

all the deltas in DS, and no token se quence that properly

Let us consider a real example of deltas from article

contains s occurs in all of the deltas in DS.

“Boston Marathon bombings”. Four deltas from one

In the deltas of Example 3.1, consecutive tokens such as“A news report,” “ripped through”, and “Boylston Street”

branch are shown below.
Example 3.1

satisfy the condition of supergram and grouped into

D1: Explosion on Boylston Street.

supergrams in Figure 4.

D2: Two loud explosions on Boylston Street.

not properly contained by any other token sequences, so

D3: Friends said explosion occurred on Boylston

the token itself is a supergram. As we can see, supergrams

Street.

are

D4: A news report explosion ripped through Boylston
Street.

capturingunchanged

However, token “explosion” ismeaningful

sequencesphraseswithintheandreflecting

scope

under

consideration.

We detect tokens and construct a token transition graph

A large number of tokens could be contained in one

from the union of all the deltas. As shown in Figure 3,

linear chain,each vertex is labeled with a token, and each edge

consecutive text. In this case, its supergram becomes too

represents at least one consecutive occurrence of twolong. Since such long supergrams do not represent changes

tokens (a bigram). We find that “ explosion ”

occurred in the scope, we should exclude th em from

and

which

come

from

a large unchanged“ Boylston Street” appear in all of D 1 ,..., D 4 . When wesummaries.

collect deltas, we need to retain stopwords in order tolikeensure readability of token sequences, that is why there

proportion in the delta set. In this case, we apply ranking

are stopwords like ‘a’, ‘on’, ‘through’ in Figure 3. Pathof tokens occurring in supergrams to find significant

contraction is to merge two adjacent nodes such that one

supergrams for summaries.

vertex is the sole destination or origin of another vertex.

phrases

3.6

On the other hand , many short supergrams
andspecific

nouns

occupyacertain

TF-IDF on supergram

As shown in Figure 4, tokens <two, loud>, <friends, said>,

TF-IDF is often used as a weighting scheme in<a, news, report>, <ripped, through>, <Boylston, Street>information retrieval and text mining. In a supergram set

are merged into new token sequences. Through updating

SS, we can calculate the TF-IDF score as weighting ofthese new tokens in the original deltas D 1 ,...,D n , we obtaineach supergram.

new deltas D’ 1 ,..., D’ n ’ .

Figure 5. According to TF-IDF scores in Figure 5, if

Details of the method are shown inlength(supergrams) >10, remove stopwords and do TF -IDF
to return SSnew, else return the initial SS. Supergrams
contain stopwords for readability, but stopwords need tobe removed when computing TF/IDF. The higher the
TF-IDF score is, the more the supergram can represent the
topic of deltas. Meanwhile, we can present supergrams
Fig. 3. Token transition graph.

that occur at a branch as a potential cause of the branch.
3.7

Three edit-contextual categories of topics

In a revision graph, one revision usually contains
multiple topics, and the amount of topics will increaseover time. We classify these topics based on which context
in the revision graph the topics represent :1.
Fig. 4. Path contraction.
3.5 Supergram
There exist token sequences that keep appearing
throughout all the deltas within a scope. We can group

Popular topic is such that it is most prominent

among all the revisions in a view. We can discover such
topics by LDA[1][10] from the revision graph.
2.

Surviving topic is a topic that appears at arevision, and continues to appear until the latest (current)revision. It can also be described as a surviving topic in
the mainstream. After a period of e dits, certain topics
become stable and survive to the latest.
3.

Extinct topic is a topic that is not surviving to

the current revision. In Example 3.1, “ Boylston Street”
belongs to this topic property.

The definition ofsurviving topics is relative to the current revision, so if
there are large amount of deletes after the current
revision, several topics may be lost and surviving topics
can be changed to extinct.
Algorithm

Fig. 6. Conceptual visualization of topics in “Boston

Algorithm ExtractPopularTopics

Marathon Bombings”

Input: all the revisions on the graph

Figure 5 shows the proposed algorithm for detecting

Output: ( token : probability )Method:topics by three categories. Figure 6 shows a conceptual

Apply LDA (Gibbs Sampling) [17]Algorithm ExtractingSurvivingAndExtinctTopics

categories are marked on the revision graph. From the

Input: A set of revisions of an article

figure, we can examine the evolution of the article over

Output: supergram set SS new
Method:Dividedepiction of three topic categories, where three topic

revisions

into

time.

mainstream(surviving) and branches(extinct) .
For each branch b, call DetectTopics(b)4. Experimental evaluation

Divide the revisions of the mainstream into scopes

4.1

Data set

and for each revision set s of the scope, callTo evaluate quality of extracted topics, we used Wikipedia

DetectTopics(s)article “Nazi Germany” as benchmark, which has 1,093

Function DetectTopics(scope s)

revisions.

As described in Section 3.3, we first executefor each scope set docleaning the revision set and then compute deltas for each1) Compute deltas by taking the diff for each ofpair of adjacent revisions. Then we apply the algorithm of

two adjacent revisions (Section 3.3)
and obtain the delta set DS (including freq) .
2)

Figure 5, to divide the revision graph into branches andscopes, and then obtain supergrams.

Construct the token transition graph from DS,

where each edge is assigned
a token. Then perform path contraction (Section 3.4).
3) Construct Supergrams
Select tokens of length greater than 2 as supergrams
and insert into set SS new .
4) TF-IDF scorefor each delta d in DS and for each supergram s
appearing in d do
if the length of s > 10, remove stopwords from s.
Compute TF-IDF weight of s,where TF is the frequency of s in d and
DF = (size of DS) / (# of deltas containing s).
return SS new
Fig. 5 The proposed algorithm for revision topic
detection

Fig. 7. Zoom-in of a part of the system output.
4.2

Result analysis and evaluation

Figure 7 shows screenshot of our system implementing the
proposedmethod,

where

each

color

of

verticescorresponds to a scope, and the first edge in each scope islabeledwith

its

topic.

The

supergramsinyellowbackground color are extinct topics and those in bluebackground color are surviving topics.

on TF-IDF. When LDA is applied on deltas orAn interesting relationship is found between popularsupergrams, the size of the training set is insufficient,

topics and surviving topics. Most of top (30+) tokens ofwhich causes that the result of LDA becomes close topopular topics are not evenly distributed in the scopes and

TF. Thus LDA on deltas is not chosen.

branches; they tend to appear in earlier revisions o f thehistory.

2.

LDA on merged revisions. In this case, we use the

The main reason is that if a topic appeared earlymerged text of the revisions within a scope for LDA.

and remained in the mainstream, the topic will have more

Since LDA can find topic distribution from a corpus

frequencies and tend to be selected as a popular topic inlarger than the other methods, LDA is expected to

LDA calculation. This matches our purpose of popular

perform better than the cases applied on deltas andtopics, since central topics of the article will be added at

supergrams.

the beginning and will not be deleted later. Surviving

3.

topics are representing a scope of the mainstream, which

TF-IDF on supergrams (Proposed method), as
described in Figure 5.

can illustrate what topics are introduced in the scope.

Table 2 shows the expected performance of three methods

To evaluate the quality of topics generated by ou r system,on the three levels of objects.

we conduct human judgment evaluation and compare with

Table 2.

two representative methods. Our problem of summarizing

three levels of objects.

edit deltas involve three levels of hierarchical objects:

Objectrevisions, deltas and supergrams. Therefore we contrast

level/scoring

our proposing algorithm with topic summarization overdifferent objects.
1.

TF-IDF on deltas (Baseline), which simply merges

Expected performance of three methods on the
TF-IDF

TF

LDA

Revisions

NeutralNeutralGood

DeltasGood

NeutralBad

Supergrams

Good

NeutralBad

TF-IDFondeltas within a scope and selects top six tokens based
Table 3.
ID
1
2
3
4
5

Baseline (TF-IDF on deltas)

7
8
9
10

LDA

on

mergedProposedmethod(revisionssupergrams )humanity, crime, trial, allied,nazi, germany, german,

spring 1945 of fall 1944 east

put, everwar, police, party

hitler,german,including,

german,nazi,

force,

german, world, hitler, war, minister, prime,

many, world, prime

hitler, italian, invasion

versialles

government, general, started,nazi,

genocide started creation general policies,

creation, genodic, policy

force, italian, invasion

government with nazis

operation, barbarossa, north,nazi, german, von, han,thiscampaign, theatre, easternkarl, war

campaign

nazi, german, td,war, party, nazi, hitler,nazi, germany, party, command, submarine,

regime, army

police, military, hitler, racial

germany,

nsdap, party
6

Comparing detected topics by three algorithms.

karl,

von,

german,

han,

nazi,

german,german,war,

force,

known

african,

south

north

force, italian, britain, war, invasion, defeat,wilhelm, force

hitler, invasion, italianwilhelm, campaign, soviet, france

reich, nazi, third, war, tr,war, nazi, karl, wilhelm,

reich,

germany

force, franzregime, power, republic failure

nazi,

war,

von,

germany,

nazi,

war,

force,

war,

europe,wilhelm,

adolf,
italian,empire,walther,

german, wiltelm

germany, von, party

invasion, campaign, battle

impure, refered, also, see,germany,

also see history germany

cultural, history

invaded, regime, unitednazi, reich, german,
hitler, minister

von,

war,

war,

reich,

nazi,

germany,

theatreglorious,
friedrich,hilter, state, goverment, karl, party, minister

minister, nazi

We randomly selected 10 scopes from article “NaziGermany.” The resulting summaries by the three methodsare shown in Table 3. To evaluate qualities of thesummaries, we asked ten volunteers to rank the results. We
use symbols ‘A’, ‘B’, ‘C’ as evaluation levels, where ‘A’ iscontent. ACM Tran. Intelligent Syst. &Technology(TIST), 2012, 3(4): 69.
[3] Chen, Yan, et al. "Emerging topic detection forthe best, and ‘C’ is the worst.

organizations from microblogs." Proc . ACM SIGIR

Table 4.

Conf.

Rank\
Methods

Results of rankings by human judgment.
Baseline

LDA(TF-IDF ononrevisions

TF-IDF on
supergrams

A(Best)

17

2

81

B

79

5

16

C(Worst)4

93

3

interesting findings have aroused our attention. First,
Baseline has 17 votes of level ‘A’ (best), and these votes
mainly concentrated in No.2 and No.9. The common pointof these two data sets is that the delta set is too small. The
smaller the delta set is, the more the result of TF -IDF on
supergrams tends to become identical to Baseli ne. Second,
as observed in No.3 and No.4, TF -IDF on supergrams
retain topic phrases better than the other methods. The
other two methods are generating topics that contain token
fragments, which makes difficult to capture the meaningof deltas.

Georgescu,

M.,

D.

D. Pham,etal.

Temporal

281 -284, 2013.

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can

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we

proposedamethodforeditsummarization on deltas of revision graphs, which can
explain changes in the revision history of Wikipedia
Our approach is based on supergrams, which

consist of consecutive tokens within a revision su bset suchas a branch or scope, so that edit contexts are reflected
into summarizations. We found that TF/IDF scoring onsupergrams has the best performance in finding usefulphrases.
In future work, we try to improve our method throughrefinement of scoring by reflecting more detailed edit
contexts.

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this

and

summarization of event-related updates in Wikipedia.

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Research

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