DEIM Forum 2019 B7-2

Detecting Evolution of Keyphrases from Wikipedia Edit History
Qianhui WEI†

Zihang CHEN‡

and Mizuho IWAIHARA‡†Graduate School of Information, Production and Systems, Waseda University Kitakyushu, Japan
E-mail: †wqh@toki.waseda.jp, ‡chenzihang @waseda.jp, ‡iwaihara@waseda.jp
Abstract Wikipedia stores edit history which records revisions of each article. From the edit history, we can extract a greatamount of information on how real world events happened and evolved. A significant event is often given a name by the
public or mass media, which is also reflected onto Wikipedia articles. Keyphrases of a sequence of events may be changed or
new keyphrases are spawn as the events evolve, which we regard as a topic transition. TextRank is an effective algorithm toextract keyphrases from word co-occurrence graphs, but it does not consider temporal trends in text stream. In our previous
work, we proposed TextRank_nfidf which considers temporal changes of word co-occurrences. In this paper, we discuss
detecting evolving keyphrases along history. We apply our temporal keyphrase extraction to several Wikipedia categories andarticles, to track attention of edits transferring as events develop. We examine long-term edit trends of relevant phrases todiscover transfer of editing attentions. Each word has its own bursting periods, which is captured as a high-score node in ourgraph. Bursting periods of words may overlap, and if one word is later succeeded by another word, such a change can be
regarded as a topic transition. We discuss methods for detecting topic transitions from temporal changes of keyphrases.
Keyword edit history, keywords extraction, TextRank, keyphrases graph
graphs represent continuously changed topic of selected

1. Introduction
prominent

articles or categories. If we slightly adjust the articles inencyclopedia on the Internet. All the revisions of eachthe article set, the main topic reflected in the node graphs

article and its related information are stored in Wikipedia

will also change. It is crucial for finding articles that areedit history. Revisions are often triggered by real world

related with a given topic in order to dig more related

events. Editors notice about events from news articles,

keyphrases about it. We discuss the necessary to design ansocial media, or some other sources, and select new facts

algorithm for selecting articles for a given topic. Section 2

to be added into Wikipedia articles. So compared withcovers related work. Section 3 is an introduction ofnews articles, Wikipedia edit history shows how an event

TextRank_nfidf

happens and evolves in a more summarized and organizedframework and requirements for detecting keyphrasemanner.

transitions. Section 5 is experiments and discussions.

Wikipedia

is

now

one

of

the

most

A number of work have been focuses on detecting andproposed

in

[ 2].

Section

4

is

the

Section 6 is a conclusion.

analyzing bursts of text stream [2][3]. In our previous
work

[2],

we

proposed

TextRank_nfidf

to

ext ract

2. RELATED WORK

keyphrases that can represent the topics of document

Liu, Ruoran, et al. [4] discussed utilizing temporal

streams in a given collection of articles. Our algorithm

information, topic information to mine evolution phases ofshows superior performance over TextRank. We contrasted

hot events. They categorize the phases of occurrences into

quality of extracted phrases with Google Trends, and thedevelopment, climax, decline, and ending during theresult shows that the keyphrases we extracted are welllifespan of hot events.

representing real world events in burst periods.

Inspired by PageRank [8], Mihalcea and Tarau [6]In this paper, we apply TextRank_nfidf to several
Wikipedia

categories

to

track

attentionofproposed TextRank, which extracts significant keywords

edits

by co-occurrence relationship between words. TextRanktransferring as events develop. We examine long -term edit

constructs an edge weighted graph and gives ranking

trends of relevant phrases to discover transfer of editingscores based on the PageRank algorithm. Bellaachia andattentions. Each word has its own bursting periods, and

Al-Dhelaan [1] added node weights to TextRank, which

the bursting word is captured as a high-score node in our

improvesgraph. Bursting periods of words may overlap, and if one

document sets. But these keyword extraction methods doword is later succeeded by another word, such a c hange

not consider temporal information

can be regarded as a topic transition. The keyphrases node

documents. In our work, we reconsider node weights andtheprecisioninextracting

keywordsof edits onintheassign smoothed edit activity levels as node weights ofdamping factor usually set to 0.85, and w j i is the edgephrases.

weight. In(V i ) is the set of nodes that point to V i , Out(V j )In 2008, J.P. Herrera et al. tackled the problem ofis the set of nodes that node V i point to. TextRank only

finding and ranking the relevant words of a document bytakes edge weights into account. This leads to a situation

using statistical information referring to the spatial use ofsuch that common words which appear many times in thethe words [5]. Shannon‘s entropy of information was usedassociated text have a high rank score.

for automatic keyword extraction. The randomly shuffled

Chen Zihang, et al. [2] proposes TextRank_nfidf which

text was used as a standard and the various measures used

reduces the rank of common words by adding a nodein the original

weight W(V i ) to original TextRank. The score function is

document

text

were normalized bycorresponding measures of random text.

defined as:

P. Carpena et al. proposed to automatically extract
keywords from literary texts through a generalization of
the level statistics analysis of quantum disordered systems[7]. They consider frequencies of the words along with
their spatial distribution along the text, and is based on theobservation

important

𝑊𝑆(𝑉𝑗 )

(2)

Here, the node weight is defined as 𝑊(𝑉𝑖 )𝑛𝑓𝑖𝑑𝑓 :𝑊(𝑉𝑖 )𝑛𝑓𝑖𝑑𝑓 = 𝑁𝐹(𝑉𝑖 ) ∗ log 2

𝐿(3)

𝐷𝐹(𝑉𝑖 )

Here, L is the number of articles appeared in the historyof the given target article set S, and DF(V i ) is the number

randomly in the text. No reference corpus is needed in thisof articles containing phrase V i . The number of articles L

approach and it is especially suitable for single documents

is changing over time. But for simplicity, L is set to be

for which no priori information is available.

the number of all the articles that ever appeared.

irrelevant

are

𝑤𝑗𝑘

distributed

whereaswords

𝑤𝑗𝑖
𝑉𝑘 ∈𝑂𝑢𝑡(𝑉𝑗 )significantly

clusteredthat

𝑊𝑆(𝑉𝑖 ) = (1 − 𝑑) ∗ 𝑊(𝑉𝑖 ) + 𝑑 ∗ 𝑊(𝑉𝑖 ) ∗ ∑𝑉𝑗 ∈𝐼𝑛(𝑉𝑖 ) ∑

words

are

The net frequency (NF) is to capture net edit activitiesof each phrase, defined below:

3. TextRank_nfidf
In this section, we describe TextRank_nfidf [2] which is

𝑅𝑇𝐹𝐷𝑗,𝑘 (𝑉𝑖 )𝑁𝐹𝑡 (𝑉𝑖 ) = 𝑚𝑎𝑥 {0, ∑𝐿𝑘=1 ∑𝑗∈𝑟𝑒𝑣(𝑘,𝑡) ∑𝐿}

𝑘=1|𝑟𝑒𝑣(𝑘,𝑡)|

used for detecting bursty keyphrases from revision(5)

Here, rev(k,t) is the set of revision IDs of the k-th

sequences.

article, created in the t-th week. NF t (V i ) represents the net

3.1 Keyphrase Extraction based on TextRank_nfidf
TextRank is a well-known method to extract keyphrases.

frequency of term V i in the t-th week before the current

It is inspired from PageRank for web page scoring.

time. RTFD j, k (V i ) measures the difference of frequency of

TextRank utilizes co-occurrence relationship between

phrase Vi between j-th revision and (j-1)-th revision in thewords to construct an edge weighted graph.

k-th article. Then the exponential moving average is

We assumethat a set S of articles, called the target article set, or

applied to smooth the net frequency, as below:

simply target set, is given. Each article in S is a sequenceof revisions. We callthe text difference of twoconsecutive revisions of one article a revision delta.
We apply part-of-speech (POS) Tagger of Apache

Here, NF 1 (V i ) is the net frequency of node V i at theOpen NLP to divide each sentence of revision deltas intocurrent week, NF 2 (V i ) is the net frequency of node V i atchunks, where chunks containing noun POS tags are

the last week and so on. The coefficient 0 ≤ η < 1 gives

regarded as phrases. We construct a graph such that eachthe damping factor.

node is labeled with an extracted phrase, and there is anedge (V i , V j ) with weight w j i , from node V i to node V j , if

With the score function of (2), the TextRank algorithm
gives scores to the nodes o f the graph.

the phrases of V i and V j co-occur in a window of maximum
M words in the text. The weight w j i is equal theco-occurrence count of V j and V i in this window.
The definition of the score function of TextRank isshown in (1).
𝑊𝑆(𝑉𝑖 ) = (1 − 𝑑) + 𝑑 ∗ ∑𝑉𝑗 ∈𝐼𝑛(𝑉𝑖 ) ∑𝑤𝑗𝑖

𝑉𝑘 ∈𝑂𝑢𝑡(𝑉𝑗 )𝑤𝑗𝑘

𝑊𝑆(𝑉𝑗 )

(1)

Here, WS(V i ) is the rank score of node V i , d is theFig. 1.

Framework of our system

6)

A quite significant keyphrase may emerge andwidely occur in articles of various categories, which

4. Detecting topic trajectory
Our objective is to track attention of edits transferring as

may not accompany with appropriate links to theevents develop. First we extract phrases which canorigin of the keyphrase. In such a case, the target set

represent bursts in revision streams using TextRank_nfidf.

S has to be reduced to more specific categories to

We regard a phrase scored high by TextRank_nfidf

reflect a more focused topic rather than a large-scale

represents a topic, and call it a keyphrase.

boom.

Bursting

periods of keyphrases may overlap, and if one keyphrase

The above discussion indicates that the selection andis later succeeded by another keyphrase, such a change canupdate of the target article set S is crucial for finding

be regarded as a topic transition. We discuss algorithms

quality keyphrases that can capture topic transition andfor detecting topic transitions from temporal changes of

evolution.

keyphrases.

requirements we plan to design an algorithm for updating

Each phrase has its own bursting periods, which is

In future work,

according to the above

the target set S along the timeline.

captured as a high-score node in the graph. New
keyphrases may spawn from existing keyphrases, as time

5. Experiments

lapses. We can detect such transition of keyphrases by1)comparing temporal changes of the phrase graphs.

We

Article set
Wecollected

all

the

revisionsfromWikipedia

can formalize criteria of topic transition by the following

category “Russian interference in the 2016 United Statesconditions:elections.” The collected revisions were created betweena)b)

Two phrases p 1 and p 2 have overlapping burst

2015/10/01 and 2018/10/01 (totally 157 weeks). There are

periods,

28 articles and 17845 revisions.

p 1 and p 2 are connected by an edge in a phrase
graph, and2)Keyphrase extraction and evolution

the first burst of p 2 is after the first burst of p 1 .

Fig.1 shows the framework of our system. We apply

Condition (b) means that p 1 and p 2 co-occur in a window

TextRank_nfidf to our dataset. Fig.2 shows the weekly

of maximum M words in one article. Since the magnitude

TextRank_nfidf score of six significant phrases “Clinton”,

of each burst is relative to the scale of articles, we have to“Trump”, “Russia”, “Dossier”, “Comey”, and “Mueller” incarefully determine criteria for topic transition. The

our dataset. It shows how rank score of a word changes asfollowing situations have to be taken into account.

events evolve.

c)

1)

2)

3)

The target article set S defines the context of a burst.

In early March 2015, Hillary Clinton was revealed that

If S is chosen as a Wikipedia category, we will

she used private email server for official communications.

observe bursts regarding the topic of the category.

This is the beginning of a chain of Russian interference

A keyphrase p may not be detected if S contains too

exposure. From week 1 to around week 60 the articles aremany articles and a burst of S can be hidden amongedited regarding Clinton’s email controversy. Starting

other popular phrases. A compact set S in which pfrom week 70, Russian Government was reported to directshows a clear burst can be regarded that the articles

email hacking to interrupt U.S. election. TextRank scoresin S are influenced by the burst of p.

of keyphrases “Trump” and “Russia” rise rapidly. Soon

If

the

targetcomputation
4)

5)

article

cost

for

set

S

becomes

TextRank

scorelarger,after that, new article “Trump-Russia dossier” was created

grows

and the word “Dossier” first appears in the dataset. At

nonlinearly.

week 82, Trump dismissed FBI Director James Comey.

A keyphrase p may incur frequent edits but later a

Mueller was appointed as special counsel overseeing

different name can be used, which should be detected

investigation into Russian interference . TextRank score ofas topic transition. However, such transition can beword “Comey” reaches its highest at around week 90 andoutside of the target set S. Thus, to detect transition

word “Mueller” at around 106. Bursting periods of

we have to update the target set.

“Comey” and “Mueller” overlap, and “Comey” is later

Keyphrases of a target set S are representing topics

succeeded by “Mueller”. It shows that the public’sof S. Also, from a bursting keyphrase p. topic

attentions are soon transferred from Comey ’s dismissal totransition to one of these keyphrases can happen.

Mueller’s investigation.

Clinton’s email controversy is the beginning of aseries of Russian inference scandal exposure. At week 60,
people only talks about Clin ton’s email controversy(Fig.3(a)). At week 70, the connection between Trump andRussia was reported to the public (Fig.3 (b)). Soon, Trump
attracted the editors’ attention and the node “trump”
becomes larger than “clinton.”

New significant nodes

like “putin,” “Russia,” “dossier” appear. At around week
90, Comey, the director of the FBI was dismissed by
Trump (Fig.3(c)). In this week, “comey,” “dismissal”
become hot nodes. Then, Mueller was appointed as special
counsel of Russian interference. Node “mueller” becomeslarger than “comey” at week 106 due to the transfer ofFig.2. Weekly TextRank_nfidf score of some importantwords

The nodes “mueller” and “comey” each has its own

We visualize keyphrases using undirected graph s
wherekeyphrases

arerepresented

public attention (Fig.3(d)).

as

nodes

bursting periods, which overlap with one another. “comey”

andis later succeed by “mueller” in a topic transition. The

relationship between keyphrases are represented by edges.

focus of the topic transits from Comey’s dismissal to

The size of nodes represents TextRank_nfidf score.

Mueller’s nomination. This is a typical example of topic

Phrases with high score are represented as large nodes.

transition.

Fig.3 (a)-(d) show graphs of weeks 60, 70, 90, and 106.

Fig. 3(a) Keyphrase graph of week 60

Fig. 3(c) Keyphrase graph of week 90

Fig. 3(b) Keyphrase graph of week 70

Fig. 3(d) Keyphrase graph of week 106

3)

Renew article set

set
Fig.5(a) is the node graph at week 90 of the originalarticle set. The main event in this graph is Trump-Russia
dossier. Comey’s dismissal appears in this graph. But
many related keyphrases are not included. Fig. 5(b) is thenode graph at week 90 of the renewed article set. The
event of Comey’s dismissal is clearly captured. Many
related keyphrases about Comey’s dismissal are shown.
The neighbor nodes of ‘comey’ include ‘rosenstein,’‘congress’, ‘russia,’ ‘dismissal,’ ‘firing,’ ‘investigation,’‘interference’ and so on. It shows that if we slightly adjust

Fig. 4 Updated article set

the article set, the topic of the node graph and keyphrases

In order to find more related events and keyphrases

will also change. The original article set is from a large

about ‘comey’ at around week 90 (corresponding with thecategory, where many events are captured in the node

event “dismissal of James Comey”), we renewed ourgraph. If we want to clearly capture the e vent that we arearticle set by adding more articles related with ‘comey’

concerned about, we can add more articles or deleteand deleting articles that do not contain word ‘comey’articles regarding the phrase to renew the article set. The(Fig.4). We use TextRank_nfidf to calculate score of

updated article set should contain topics more closely

keyphrases again in the new article set and get a new node

related to the selected keyphrase and burst.

graph which keep ‘comey’ as the central.

6. Conclusion and Future work
In this paper, we utilize TextRank_nfidf to extract
keyphrases and discuss detecting evolving keyphrases
along

history.

We

showweeklyresultsofrankedkeyphrases and explain how the scores are affected as
eventsdevelop.

We

visualize

keyphrases

and

their

relationship using keyphrase graphs. Node graphs areclosely reflecting the topics of given article set. We findthat if we slightly adjust the article set, more keyphrases
related with given topic can be detected. In the future, wewill discuss an algorithm for article set update.

References
Fig. 5(a) node graph at week 90 of the original article
setFig. 5(b) Node graph at week 90 of the original article[1] Bellaachia A, Al-Dhelaan M, “NE-Rank: A Novel
Graph-Based Keyphrase Extraction in Twitter, ” In
Proc. Conf. Web Intelligence & Intelligent Agent
Technology, 2012, pp. 372-379.
[2] Chen, Z. and M. Iwaihara, “Detection of Bursty andSignificant Keyphrases from Wikipedia Edit History, ”
Proc. IEEE Int. Conf. Big Data and Smart Computing(BigComp2019), Feb. 2019.
[3] Christiansen, L., Schimoler, T., Burke, R., &Mobasher, B, “Modeling topic trends on the social
web using temporal signatures, ” In Proc. 12th Int.
Workshop on Web information and data management,
Nov 2012, pp. 3-10.
[4] Liu, Ruoran, et al, “Mining phase evolution for hot
topics: A case study from multiple social media
platforms,” IEEE Int. Conf. IEEE, Systems, Man, andCybernetics (SMC), 2017, pp. 2814 -2819.
[5] J.P. Herrera, P.A. Pury, ―Statistical keyword
detection in literary corpora‖, The European physicaljournal, 2008

[6] Mihalcea R, Tarau P, “TextRank: Bringing order into
text,” In Proc. Conf. Empirical Methods in Natural
Language Processing, 2004.
[7] P. Carpena et al., ―Level statistics of words-Finding
keywords in literary texts and symbolic sequences ‖,Physical Review E, 79, 03512(R), 2009[8] Page, Lawrence, et al, “The PageRank citation
ranking: Bringing order to the web,” Stanford
InfoLab, 1999.