DEIM Forum 2013 A10-4

Context-based Word Clouds of Presentation Slides for Quick Browsing
Yuanyuan WANG†

and

Kazutoshi SUMIYA‡†Graduate School of Human Science and Environment, University of Hyogo
1-1-12 Shinzaike-honcho, Himeji, Hyogo, 670-0092 Japan
E-mail: †{ne11u001@st, sumiya}shse.u-hyogo.ac.jp,
Abstract With the advent of online education services such as iTunes U and SlideShare, students are able to access various
presentation files from many universities for study purposes. However, self-learners retrieving such files still need support todecide which files in candidate presentations are worth learning because most of the candidates are similar; it can be difficult to
identify differences in the candidates. We consider that presentation flow is very important in presentation contents for
identifying the differences in the candidates. In this paper, we introduce a quick browsing system to help a user effectively
decide whether a presentation content is appropriate for his/her specific learning needs. Our method provides context-based
word clouds that summarize slide information to help the user visually understand the context of one presentation content. In
our method, words important to the “presentation context,” that is, the relevant information on the slides, are first extracted
based on components of the presentation content (i.e., intra-slide structure and inter-slide structure). Then the word cloud isgenerated by weighting the words within the presentation context for each slide. Finally, the words in the word clouds areinteractively presented with visual effects that reflect their semantic meanings.
KeywordPresentation slides, Context-based word cloud, Quick browsing, E-learning,
titles only, users cannot grasp specifics of the content

1. Introduction
Presentation slides (e.g., PowerPoint, Keynote) are now(see Fig. 1 (a)). In addition, important words of slides that

one of the most frequently used tools for educational

summarize slide information simply based on word

purposes. A huge amount of slide-based lecture material,

TF-IDF scores appearing in candidate presentations canoften prepared from teaching material used in actual

destroy the implicit relevant information between slides

classes at universities or other educational institutions, isand decrease the relevance of words in slides to the1andoverall context (see Fig. 1 (b)). This makes it difficult to

SlideShare . Thus, not only students who missed a lecture

understand the context of each candidate presentationor presentation, but also anyone interested in the topic canwhen choosing desired files.

freely shared on Web sites such as iTun es U
2study the presentation on their own. Therefore, techniques

As depicted in Fig. 2, we present a quick browsing

are in demand that will efficiently find one or moremethod that considered the context of presentation for

appropriate slides with content worth learning from the

supportingavast numbers of presentations available. Although manyeffectively

cantechniques for searching and recommending presentation

context-based word clouds for each slide by weighting theslides have been proposed, some problems remain fromwords within the context of the presentation (i.e., thethe viewpoint of understandability for users browsin g

intra-slide structure and inter-slide structure) and 2)

search engine results. One problem is a search engine does

determines transitions between the generated word clouds

not consider context when matching user query words

based on relationships between the words in serial slides

within presentation content, leading to a large number ofthat include what users need to browse with our novel

candidate results are similar. Another problem is thequick browsing interface. In order to achieve our goal, wedifficulty of general quick bro wsing for visualizing results

derive the intra-slide structure that slide structure byof presentation contents, that is, when browsing slide

focusing on the level of indentation in the slide text, and

user
be

to

decidepresentation

implementedby1)

contents
generates

determine the inter-slide structure that relationships as1
2

http://www.apple.com/jp/education/itunes -u/
http://www.slideshare.net/

links between slides by considering words that appear atdifferent indentation levels in the structure of other slides.

Fig. 1 General quick browsing methods

For example, ‘Keys’ appears in the body of text in a slide
entitled ‘Relational model’, which related to the slide
entitled ‘Relational database’ and ‘Relational model’appears in the body of text in it. ‘Keys’ is the title of otherslide that is related to the slide entitled ‘Relationalmodel’.

Fig. 2 Conceptual diagram of quick browsing method

In this paper, we define presentati on context to mean
the context for the slide in a presentation, represented by

and Section 5 introduces a prototype application for quick

the relevant information on the slide and allowing forbrowsing based on our method.

relevant information from the rest of the presentation that

concludes this paper with suggestions for further work.

Finally, Section 6

is not included on the slide. We define two types ofpresentation context for a slide: link context and structural

2. Related Work

context, based on the relationships between slides andOur quick browsing interface is based on a word (tag)slide structure, respectively. Using presentation context,

cloud, a visualization using a set of words. The size andwe can generate context-based word clouds of slides bycolor of each weighted words in the presentation content

weighting words in the presentatio n. There are twobased on our proposed method. Then, o ur work is directly

concepts that are particularly helpful when quick browsing

related to the research efforts in two areas: text analytics

presentation content:

and information visualization. We also review the effectsPresentation

flow:

link

or

break

[1].

Often,

of a word cloud on browsing tasks.

presentations are formed of a chain of slides such

In the area of text analytics, researchers have developedthat one slide links to the next. Sometimes, however,a number of approaches to text summarization , of whicha slide will move from the point in a previous slide tothere are two

maintechniques:

a completely different point. In this case, there is a

keyword-based

textsummarization.

break between them.

approaches identify the most salient sentences in a

Highlight points: semantics. This occurs when one

document [2], [3]. For example, Murai and Ushiama [4]sentence-based andSentenced -based

slide describes a point from a previous slide in detail.

proposed a browsing method that presents users with a

In this case, our approach presents words interactively

review-based recommendation of attractive sentences in afrom one slide to another as a streaming word cloudnovel. However, it may be time consuming for users toreflecting the flow of points in the slides, helping users toread several sentences per document especially whenselect desired presentations from search engine results

handling a large number of documents. Alternatively,

easily and effectively.

keyword-based methods summarize documents by topics,

This paper is organized as follows. The next section

each of which is characterized by a set of keywords [ 5],reviews related work, and Section 3 describes how to[6], and [16]. Our quick browsing method is built on thedetermine presentation contexts for slides. Section 4

latterpresents the generation of context -based word clouds andsummarization

the determination of transitions between the word clouds,

visualization. Moreover, we provide users with a novel

method,but

its

results

focus
throughisonenhancing

the

word

cloudsandinterface with visual transitions to present the dynamic
word clouds.
In the area of information visualization, researchers
have developed various visualization approaches to text
analysis. These systems can be classified into two
categories:

metadata-based

andcontent-based

text

visualization. Metadata-based text visualization focuseson visualizing the metadata of text documents. In email
analysis for instance, metadata-based text visualization
can use a time-based visualization to explain text
summarization results derived by a text analytic engine [7],or a relationship-based visualization of email senders andFig. 3 Presentation context for slide yreceivers [8]. For content-based text visualization, Viegas
et al. [9] used Themail to visualize keywords based on
keyword TF-IDF scores in an email collection. Similarly,
Strobelt et al. [10] used a mixture of images and
TF-IDF-based keywords to create a compact visualizationof a document. More recently, Chen et al. [11] and Iwata
et al. [12] focused on visualizing document clusteringresults.

In

contrast,

othershave

concentrated

on

representing text content at the word or phr ase level,
including TextArc (www.textarc.org) , WordTree [13],Phrase Net [14], and FeatureLens [15]. Our work focusesonvisualizing

presentation

contents

bygenerating

context-based word clouds of slides, and presenting
transitions between the context -based word clouds.
Several studies have been conducted on searching andbrowsing tasks. In their study of the usefulness of word
clouds in information-seeking tasks, Sinclair et al. [19]concluded that such an interface is more useful forbrowsing tasks than for searching tasks. Although ourinterface can support browsing, we are most interested in
how our interface can support passage comprehension.
The visual features of a word cloud can impact the user ’s
performance. For example, Bateman et al. [20] examin ed
the effect of nine visual properties of a word for the taskof determining the most important word based on visualappearance. Halvey and Keane [21] compared word clouds
by asking participants to find and select specific words inboth interface types. Their studies revealed that the fontsize and font weight had strong effects on the word
selection

speed.

Then,

in

our

context -based

cloud

generation, we decided to explore the visual feature of
font sizes of the weighted words.

slide: link context and structural context, based on therelationships

slidesandslide

structure,

respectively. We define the slide whose presentation
context is discussed to be the target slide. We construct
the slide structure based on the indentations in the slide
text. The slide title is the first indentation level; the first
item of text within the slide body is the second indentationlevel, and the depth of the sub -items increases withindentation level (third level, fourth level, etc.). Non-text
objects, such as figures or tables, are considered to be at
the same indention level as the surrounding text.
3.1. Determination of Link Context for Slides
The link context for a target slide consists of links andanchors (similar to hyperlinks in Web pages) related to thetext body and titles of other slides. They refer to words inthe title of the target slide and titles of other slides that
contain words in the body of the target slide. They also
indicate from what type of content the target slide isreferred. We extract the link context of the target slide byfinding the same words at different levels in the target andother slides.
For a given bag of words M in the title and a given bagof words N in a level in the body of the target slide, words
in the titles and levels in the body of other slides are
extracted: …, T 2 , B 2 , T 1 , B 1 . Here, T i is the title of slide iand B i are the words in a level of the body of slide i. If B i
corresponds to M, B i can be considered as a link anchor.
Then, B i links to the target slide that the words in B i andits slide title T i belong to the link context for the target
slide, while the words in B i are similar to that in M. This iscalculated using the Simpson similarity coefficient [17] ,as

3. Determination of Presentation Contexts for

between

Sim(B i ,M)=|B i ∩ M|/min(|B i |,|M|).

When

Sim(B i ,M)exceeds a predefined threshold, the words in B i and M are

Slidessimilar. Meanwhile, if N corresponds to T i , N can be

We determine two types of presentation context for aconsidered as a link anchor. Then, N links to the slidetitled T i in that the words in T i belong to the link context

generated from words extracted from the presentation

for the target slide, while the words in N are similar to that

context by weighting the words to determine font size. For

in T i .

each type of presentation context, we calculate the degree

In Fig. 3, the link context for slide y (in blue portions)of the words that 1) appear close to the target slide and 2)

shows that slide y explains about “Relational Database,”

appear frequently near the target slide but less frequently

which is referred to on slide x as a subheading ofaround other slides.

‘Introduction,’ and the subheading ‘Tables’ in slide y is

Let us consider each word of target slide S as a relevant

described in slide z.

object, denoted by o. The degree of the relevant object o

3.2. Determination of Structural Context for Slides
The structural context for the target slide consists offor the presentation context P(S) is defined as follow:

lower, current, and upper levels of the target levels

W (o, P( S )) 

corresponding to the link context in the target slide, anddensity (o, P( S ))dist (o, S )(1)lower, current, and upper levels of the link context inHere, density(o,P(S)) is the density of the relevant

other slides based on slide structure. When the target slide

object o for the presentation context P(S), and dist(o,S) is

does not have a link context, we take the title of the target

the distance between o and S. Intuitively, density(o,P(S))

slide as the target level, and then we extract the structural

means how densely the same word as o appears in thecontext for the target slide that consists of the levels

presentation context P(S). If the same words as o appear

below the title in the target slide.

frequently in P(S) but less frequently in other presentation

For a given bag of words N at a level in the body of thecontexts, density(o,P(S)) becomes large. Suppose that S istarget slide, words in the lower, current, and upper level ofthe k-th slide among all slides (the target slide). The

N are extracted: …, l 3 , l 2 , l 1 . Here, l j represents a bag ofdensity of o in P(S k ) is calculated as follows:

words at a particular indention level j. When l j and N are
the current level in the target slide, the words at level l j + 1

density (o, P( S k )) are at the lower level of N and l j- 1 are at the upper level of

N A( o, P ( Sk ))N A( o,U )(2)

N. Therefore, the lower, current, and upper levels of thewhere A(o,P(S k )) is a set of relevant objects representing

link context in other slides are extracted in the same way.

the same word as the object o in the presentation context

In our method, when the target level in the target slideP(S k ), and A(o,U) is the set of relevant objects in thecorresponds to the link context in more than one slide, we
just extract the link context of the slide nearest to thepresentation context of all slides : U=P(S 1 )∩ P(S 2 )∩ ….
N A( o, P( S k )) and N A ( o, U) represent the number of objects intarget slide. The link context and structural context are

A(o,P(S k )) and A(o,U), respectively. Because it is difficult

extracted

surrounding

to identify the set U due to mutual dependencies between

information, containing enough words to characterize thethe presentation contexts, we approximate the set U as thepresentation context. Therefore, the presentation context

set of relevant objects of all slides.

withinaminimalrange

ofexpresses presentation flow and highlight points well.

The distance dist(o,S) indicates the strength of theFig. 3 illustrates the structural context for slide y (inassociations between the relevant object o and the target

yellow portions), where ‘Tables’ is related to ‘RDBMS,’

slide S, and is defined for each type of presentation‘Keys,’ and ‘Columns and rows’ at the upper, current, andcontext as follows:

lower levels in slide y and ‘Tables’ includes a link at a

Distance

lower level, ‘Tables contain records (rows),’ in slide z;

relationships from the target slide S to the relevant‘Relational Database’ includes a link to a lower level at

object o.

‘RDBMS’ in slide y, and ‘Relational Database’ is related

Distance in structural context: The number of parent,

to current and lower levels at ‘Datab ase’ and ‘Software

brother, and child nodes to be followed from the target

system’ in slide x.

levels in the target slide S to the relevant object o.

in link context:

The number of link

We generate context-based word clouds of slides by

4. Presentation of Context-based Word Clouds
4.1. Generation of Word Clouds of Slides
To present a streaming context-based word cloud thatreflects the semantics of the words, slide word clouds are

extracting the words based on the ratio of the degree of
each word and the highest degree of the word in each slide.
We also sort the words into three different font sizes asfollows:

Fig. 4 Screenshot of quick browsing interface
W (c, P( S ))
 W (c, P( S ))STag (c, P( S ))  c
 1 ,
2 
W
(
c
,
P
(
S
))
W
(
c
,
P
(
S
))


max
 max



W (c, P( S ))
 W (c, P( S ))MTag (c, P( S ))  c
 2 ,
 3 
Wmax (c, P( S ))
 Wmax (c, P( S ))
 W (c, P( S ))
LTag(c, P( S ))  c
 3 
 Wmax (c, P( S ))


Fig. 5 Screenshot of our presentation for this paper(3)

word cloud of the target slide.
Generalized relationship: bodies of other slides
belonging to the link context for the target slide in(4)

the word clouds. The word cloud of the target slide
contains the words about the link context in the(5)outline given in the word cloud of the other slides.

In Eqs. (3), (4), and (5), W(c, P(S)) is the degree of c

Parallel relationship: titles of other slides in theand W ma x (P(S)) is the highest degree of the word in S using

word clouds belonging to the link context that link toEqs. (1) and (2). Including too many words in each word

the current levels in the target slide, these word

cloud does not help users to browse them effectively, so

clouds of other slides are parallel with each other.

we extract c in S such that the ratio of W(c, P(S)) and

W ma x (P(S)) is greater than a threshold (i.e., 0.25). STag(c,P(S)), MTag(c, P(S)), and LTag(c, P(S)) are the groups ofIndependent relationship: slides do not have a linkcontext for each other in their word clouds.

To

present

dynamic

word

clouds,the transitions

weighted words to be displayed in small, medium, anddiscussed here explain the kinds of visual effects added tolarge font size such that the ratios satisf y Eq. (3), Eq. (4),the relationship types, reflecting presentation flow orand Eq. (5), respectively. In this paper, we empirically set

highlights. Presentation flow consists of many chains of

the values of the thresholds to be θ 1 =0.25, θ 2 =0.50, andserial slides such that each chain and each transfer switch

θ 3 =0.75. Although, in general, the word position is

between chains must be presented. For one chain of serial

important for a word cloud, in this paper, our dynamic

slides, detailed, generalized or parallel relationships exist

word cloud instead places the word randomly so that the

between them. For a transfer switch between different

user is not biased to any specific terms bas ed on theirchains of slides, the independent relationship exists

placement position.

between them. Highlights are the words belonging to theWordlink context of one chain in detail. The effects for three

CloudsBased upon the presentation contexts for slides, wetypes of transitions between the words in the generated

4.2. DeterminationofTransitions

betweenpresent dynamic word clouds with visual effects thatword clouds are as follows:

Font size changes: a shrinking or expanding effect isreflect the relationships between words interactively. For

set between serial slides when presenting one chain

this purpose, we use relationships between words in thein the presentation flow. When the font sizes of theword clouds similar to the relationships between slides

words in the current word cloud are smaller thandefined in our previous work [18], which fall into four

those in the previous one, a shrinking effect is

types based on the presentation contexts for slides:

implemented. When the font sizes of the words in theslidescurrent word cloud are larger than those in thebelonging to the link context for the target slide inprevious one, an expanding effect is applied after a

the word clouds. The word clouds of the other slides

shrinking effect. Users can easily understand thathave more information about the link context than thethey are following a chain o f slides where the words

Detailed

relationship:

titlesofotherFig. 6 Example of extracted words for generating context -based word clouds of slides

are mentioned.

size (i.e., 20pt) in a dynamic word cloud display. When aColor changes: a coloring effect is set between serialuser moves a seekbar to turn over slides, and the weighted

slides to highlight detailed points in one chain of thewords belonging the word clouds of slides with their fontpresentation flow. For a current word cloud, thesizes (i.e., small: 30pt, medium: 40pt, large: 5 0pt) arewords belonging to the next word cloud, which are

dynamically presented with visual transitions in the word

described in detail on the next slide, are drawn in red.

cloud display. We also considered that figures or tables are

Users easily see that these words are highlighted inimportant visual objects in presentations, we then attempt

one chain. When the highlighted words in the current

to build an interface is shown in Fig. 5, it has both aword cloud are not detailed in the next word cloud,dynamic word cloud display and a figure (table) display.

these words are drawn the default color (black ).

In the future, we plan to attempt to build other kinds of

Switching: a dissolve effect is applied to a transfer

interfaces to express the presentation content well.

switch between different chains in the presentation
flow. The current word cloud disappears and the next

5.2. Validity of Generated Context-based Word Clouds
We confirmed our context-based word cloud generation

word cloud appears gradually in its place. Users

method by using four presentations from our dataset, P A ,easily grasp that a transfer switch has occur red.

P B , P C , and P D . In here, P A 3 and P B 4 are online lecture
contents related to database; P C and P D are academic

5. Application

presentation contents from DEWS workshops for members

5.1. Prototype System
In this paper, based on the method described above, weof the society. We show an example of extracted weighted

built a novel quick browsing interface to support users togenerating context-based word clouds of P A named

quickly gain a broad understanding of presentation“Introduction to Relational Databases” (see Fig. 6).

words

with

their

valuesanddetermined

sizes

for

contents (see Fig. 4). The font size of each word is set to

In this example, slide 3 entitled “Relational Database”

be the degree of the word in presentation contexts. Thisthat ‘relational’ and ‘database’ are important in general.

interface also uses color to visualize the detailed points.
Users can specify any presentation content for quickbrowsing from the results, and the browser presents allwords from all word clouds of slides with an initial font

3
Portland State University:
http://web.cecs.pdx.edu/~howe/cs410/lectures/Relational_Intro_
1.ppt
4
Atilim University:
http://www.atilim.edu.tr/ ~mrehan/Chapter¥%203.ppt

Fig. 7 Examples of transitions between word clouds (P A and P B )However, in our method, we considered the context offrom slide 5 to 7 in P B , the font sizes of ‘table,’slide 3 that ‘key’ and ‘table’ have high value in slide 3. In‘relationship,’ and ‘entity’ are increased in slide 6, and the

addition, for slides 4 and 5, we can extract weighted words

font sizes of the words such ‘SQL,’ ‘language,’ and ‘data’such as ‘relational’ and ‘database’ that are not included inare increased in slide

slides 4 and 5, but these words are related to them.

‘relationship,’ and ‘SQL’ are drawn in red in slides 5, 6,

Therefore, we considered that users can grasp the flow ofand 7, respectively.

slides 3 to 5 about ‘tables and keys in relational databases’

7. In particular, ‘metadata,’

In the case of P A , we find that ‘table’ and ‘key’ are corewell.

points in the flow of slides 3 to 5, the presentation

5.3. Application Examples

explains ‘tables and keys in relational d atabases,’ detailed

When a user wants a presentation about ‘rela tional

relationships exist between ‘table’ in slides 3 and 4, anddatabase overview: tables and keys,’ he/she can specify‘key’ in slides 3 and 5. There is also a parallel relationshipany presentation content for quick browsing from thebetween ‘table’ and ‘key’ in slides 4 and 5. For P B ,results. An example of browsing the dynamic word clouds‘metadata,’ ‘relationship,’ and ‘SQL’ are core points in theof two presentations from our dataset, P A and P B , is shownflow

in Fig. 7. In this case, the user browses the flow of slide 3‘characteristics of data in relational database,’ and ato slide 5 in P A named “Introduction to Relational

parallel

Databases,” and the flow of slide 5 to slide 7 in P B named‘relationship,’ and ‘SQL’ in slides 5, 6, and 7. Therefore,“An Introduction to Relational Databases.” When the user

P A is worth learning in that it better meets the user ’s needs.

moves the seekbar to go from slide 3 to 5 in P A , the font

Although we confirmed that our proposed dynamic word

sizes of ‘table,’ ‘database,’ ‘relational,’ and ‘row’ areclouds enables a user to effectively and easily selectincreased in slide 4, and the font sizes of ‘key,’ ‘foreign,’

presentations with contents that meet his/her needs, weand ‘primary’ are increased in slide 5. In particular, ‘table’

encountered difficulties when presentation contents (i.e.,and ‘key’ are drawn in red in slides 4 and 5, respectively.

P A and P B ) had a similar title.

On the other hand, when the user moves the seekbar to goofslides5

torelationship

7,

the

existspresentation
among

explains‘metadata,’

We need to consider how best to present the differences

in similar presentation content s (e.g., different topics orsame topics with different context information, etc.).
Additionally, we must provide a quick browsing interface
thatcomparessimilarpresentation

contents

simultaneously with the differences clearly marked.

6. Concluding Remarks
In this paper, we proposed a quick browsing method forpresentation content that uses dynamic word clouds topresent words interactively with visual effects to help auser visually understand the context of content within apresentation. We described how presentation context canbe determined from slide structure and the relationships
between slides. In order to generate context -based word
clouds of slides, we extracted weighted words frompresentation context, and then presented dynamic word
clouds with transitions that highlighted the relationships
between slides. Finally, we confirmed our context -based
word cloud generation method with four presentat ion
contents and shown some application examples.
In the future, we plan to develop various other visual
effects for presenting transitions in the word clouds tohelp users intuitively understand presentation content. We
also plan to develop a quick browsi ng interface that
presents

thecontentsofmultiplepresentations

forcomparison. We have to evaluate the usability of our quickbrowsing interface to confirm that it can enable users togain a broad understanding of presentation contents meettheir needs from our collected 16 online presentation
contents related to database lectures (from universities in
America) effectively and easily.

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