DEIM Forum 2014 A4-2

An Exploratory Search Method for Presentation Contents
based on User Browsing Behavior
Yuanyuan WANG† , Yukiko KAWAI†† , and Kazutoshi SUMIYA†† Graduate School of Human Science and Environment, University of Hyogo
1-1-12 Shinzaike-honcho, Himeji, Hyogo 670-0092, Japan†† Faculty of Computer Science and Engineering, Kyoto Sangyo University
Building ♯14, Motoyama, Kamigamo, Kita-ku, Kyoto 603-8555, Japan
E-mail: †circl.wang@gmail.com, ††kawai@cc.kyoto-su.ac.jp, †††sumiya@shse.u-hyogo.ac.jp
Abstract MOOC is a crucial platform for improving education; students are able to browse various educationalpresentation contents through the Web. Any single presentation content can only cover a small fraction of knowledge in a speciﬁc domain, and thus oﬀers a limited depth of information. Students then have to go through various
presentation contents, but this would be time-consuming and diﬃcult to explore. Therefore, we propose a novel
exploratory search tool for presentation contents based on a meaningfully structured presentation by using slides,
called an interactive poster. The interactive poster places textual and graphic elements of slides in a structural
layout with a zooming user interface by semantically analyzing the slide structure. Through this, our exploratory
search can support students interactively browsing an interactive poster with their operations, for retrieving andnavigating information from other presentation contents maps students’ speciﬁc needs by considering the students’browsing behavior on the structure of the interactive poster. In this paper, we discuss two types of exploratory
search, (1) topic focused searching based on well-matched browsing behavior that enables users obtain details of
speciﬁc topics; and (2) exploratory browsing based on distributed browsing behavior that enables the users ﬁnd
various relevant information on topics of interest.
Key words exploratory search, browsing behavior, presentation contents, interactive poster

1. Introduction

terface (ZUI) as an alternative to the traditional slides. This
interface permits the canvas format to support the creation

Slide-based presentation tools, such as Microsoft Pow-of expressive layouts, which enables users interactively op-

erPoint or Apple Keynote is now one of the most fre-erate the presentations. Another problem is any single edu-

quently used tools for educational purposes currently. A huge

cational presentation material only cover a small fraction ofamount of slide-based educational materials for MOOC, areknowledge in a speciﬁc domain by a given query, and thus

freely shared on Web sites such as SlideShare（注 1） and Cours-

oﬀers a limited depth of information. The users then have to

（注 2）

era

. Thus, not only students who missed a lecture orgo through various presentation materials for their learning,presentation, but also anyone interested in a topic can study

but this will be time-consuming and diﬃcult to ﬁnd relevant

the presentation on their own. Therefore, techniques are

information from multiple presentation contents. Therefore,

in demand that will eﬃciently ﬁnd appropriate information

users will be required to browse them in structural layouts

worth learning from the vast numbers of presentations avail-with ZUIs, and easily explore information meets the users’

able. Although many techniques for searching and recom-

speciﬁc needs by considering user browsing behavior.

mending presentation slides have been proposed, some prob-

As depicted in Figure 1, we present an exploratory searchlems remain from the viewpoint of exploratory search. One

tool that generates a meaningfully structured presentation

problem is current slideshow mode of presentations does notby using the presentation slides, which is called an inter-allow users operate freely on the presentations. Recently,

active poster. With our exploratory search, (1) users canPrezi（注 3） provides an inﬁnite canvas with a zooming user in-interactively browse an interactive poster is generated by us-（注 1）：http://www.slideshare.net/

ing presentation slides, therefore, (2) the interactive poster（注 2）：https://www.coursera.org/

can explore information from other presentations by consid-（注 3）：http://prezi.com/

ering user browsing behavior; and (3) represent and navigate

to convey complex relationships among slides. MultiPresenter, integrates support for a second slide display so thatmultiple slides may be related in space as well as time [6].
Although the interactive poster does not adopt the dualaudience-display paradigm, it addresses the need to navigate through elements dynamically during the presentation.
NextSlidePlease [10] creates and delivers slideware presentations. The interactive poster is similar to this work, as we
utilize a structural layout with the ZUI, to allow users interactively browsing and automatically navigating for users.
Exploratory is constantly being changed and shaped by a
Figure 1 Conceptual diagram of an exploratory search tool

range of related research. On the web “the need behind thequery” might be Informational, Navigational, and Transac-

information meets the users’ speciﬁc needs. To achieve ourgoal, an interactive poster can be implemented by 1) semantic structure analysis of elements (i.e., textual and graphic
elements) in slides and semantic relationships between them;and 2) structural layouts with zooming and panning transitions for organizing elements based on a basic idea of Prezi.
In semantic structure analysis, we ﬁrst extract elements byexamining the presentation context of the particular elements in the slides.

The semantic relationships between

these elements are determined using implicit hyperlinks inslides, based on a slide structure. Speciﬁcally, we derive
the slide structure by focusing on the itemized sentences inthe slide text. There are various types of structural layouts
for constructing an interactive poster, such as hierarchicalstructure, stacked Venn, and pyramid structure. In order toprovide an overview of the content, we utilize a hierarchicaltional [2]. Bates [1] suggests that browsing is a cognitive andbehavioral expression of exploratory behavior and she claims
that it has four elements: (1) glimpse a scene; (2) target
an element of a scene visually and/or physically; (3) examine items of interest; and (4) physically or conceptually acquire or abandon examined items. Therefore, our interactive
poster according to this, oﬀer an overview (glimpses), theability to operate the content through various presentations(exploratory browsing). Choo and colleagues [4] developed
a model of online information seeking that combines bothbrowsing and searching. It suggests that much of Ellis’s
model [5] is already implemented by components currently
available in Web browsers. We then applied this model forsearching presentation contents by considering user browsing
behavior on the interactive poster.

3. Analysis of Presentation Contents

structure, combined with a stacked Venn for an interactive
poster. Finally, our interactive poster is generated based on
semantic relationships, using a ZUI, which can enable users
to explore the presentations easily and eﬃciently.
The next section reviews related work. Section 3 describes
our semantic structure analysis model. Section 4 explains thegeneration of interactive poster with zooming and panningtransitions. Section 5 presents two types of our exploratory
search for presentations. Finally, Section 6 concludes thispaper with suggestions for future works.

In this section, we describe a semantic structure analysis model for extracting elements of presentation slides anddetermining the semantic relationships between them.
3. 1 Element Extraction
There are two important elements, i.e., textual elements
and graphic elements, from presentation slides based on itemized sentences of bullet points in the slide text. We deﬁne
the slide title is the 1st level, the ﬁrst item of text within theslide body is the 2nd level, and the depth of the sub-items
increases with indentation level (3rd level, 4th level, etc.).

2. Related WorkA variety of applications address the weaknesses of thecurrent slideware tools in the presentation and authoring
domains. Our approach in an interactive poster builds on
the strength of exploratory search. Automated generative
tools also address the issue of presentation layout and structure. They instead create media artifacts intended to beviewed non-interactively [3], [8]. In the presentation deliv-

3. 1. 1 Textual Elements
We deﬁne textual elements as topics that focus on thenouns in slides. Based on the presentation context, a topic
can be described as a learning point with multiple nouns thatfrequently appears at the higher levels (i.e., the slide title) inneighboring slides. Initially, we extract noun phrases using alanguage analysis toolkit MSR Splat（注 4） based on the XML
ﬁles of slides. The topic that appears in the title of a slide

ery realm, recent research has addressed the question of how（注 4）：http://research.microsoft.com/en-us/projects/msrsplat/and the body of other slides can be considered to indicate
its context in a presentation. Then, we extract topics bylocating the same noun phrases in diﬀerent slides, at variedlevels. If a noun phrase k appears at diﬀerent levels in slides
si and sj , then k is a candidate for being one of the topics
T in the presentation. The steps to determine T using k is
explained here, which is presented, in si and sj .
T = {(k, si , sj )|lmax (k, si ) ̸= lmax (k, sj )}(1)

where, T is a bag of noun phrases that can be considered
as candidates for topics. lmax (k, si ) is a function that re-

Figure 2 Tree representation of a sample presentation content

turns the highest level of k in the slide si . For instance,
when the highest level is the title, i.e., the 1st level of si ,as a rooted ordered tree D = (N, E) with a set of nodes Nthen lmax (k, si ) returns 1; and when the highest level is theand a set of edges E ⊂=N × N . There exists a distinguished3rd level of sj , then lmax (k, sj ) returns 3. When k appears

root node from which the rest of the nodes can be reached byat diﬀerent levels, k is determined as a candidate for topics

traversing the edges in E. Each node n, except the root, hasprovided lmax (k, si ) is not equal to lmax (k, sj ). Then, thea unique parent node, it of the document tree is associatedweight of k in T is deﬁned using the levels of k, and thewith a logical component, such as < title > or < sections >,distance between slides si and sj , as follows:

based on an XML ﬁle in the given presentation. There is a1
I(k) =+
lmax (k, si )∑k,si ,sj ∈T

1
1
·
(2)
lmax (k, sj ) dist(si , sj )function words(n) returns the representative noun phrasesof the corresponding component in n. A partial tree of Dwith a given noun phrase as its root is deﬁned as a fragment

where lmax (k, si ) indicates the weight of k in si , it returns

f . It can be denoted as f ⊂=D. A slide is a fragment by thethe highest level of k in slide si by Eq. (1). dist(si , sj ) cor-slide title. In Figure 2, < n1 , n2 , n3 > is the set of nodes in

responds to the strength of the association between si andslide 2 and a fragment of the sample document tree.

sj , and it denotes the distance between si and sj . Thus, if k

To formally deﬁne the semantic relationships between theappears at a high level in si and sj , and the distance between

noun phrases from the extracted elements, we ﬁrst deﬁne op-

si and sj is short, the weight I(k) of k is high.

erations on fragments, and sets of fragments using a pairwise

3. 1. 2 Graphic Elements

fragment join [9]. Let Fx and Fy be two sets of fragments in a

When compared to pure textual elements, ﬁgures are more

document tree D of a given presentation, then, the pairwise

attractive, appealing and informative from a psychologicalfragment join of Fx and Fy , denoted as Fx ▷◁ Fy , is deﬁned

standpoint. Based on the study of search results presenta-

to extract a set of fragments. This set is yielded by comput-

tion [7], it can be noted that summaries with ﬁgures assist

ing the fragment join of every combination of an element inin quicker understanding of the results, thereby helping in

Fx and an element in Fy , in pairs, as follows:

arriving at relevant judgments faster. Therefore, we deﬁnegraphic elements as ﬁgures corresponding to the topic candi-

Fx ▷◁ Fy = {fx ▷◁ fy | fx ∈ Fx , fy ∈ Fy }(3)dates in slides, given that the noun phrases in the surround-Figure 3 illustrates an example of operation for pairwise

ing text of the ﬁgures are similar to the topic candidates. We

fragment join. It refers the sample document tree in Fig-

considered that the ﬁgures used to describe the content inure 2. For the given two noun phrases x = nutrition andslides, and a slide title can be a subject of the content. Wheny = f ruit, where Fx = {< n3 >, < n5 >, < n20 >},the similarity exceeds a predeﬁned threshold by calculating

Fy = {< n4 , n5 , n6 , n7 >, < n19 >}, Fx ▷◁ Fy pro-the Simpson similarity coeﬃcient, the ﬁgures are recognizedduces a set of fragments {< n3 >▷◁< n4 , n5 , n6 , n7 >,as the corresponding images of the topic candidates.

< n5 >▷◁< n4 , n5 , n6 , n7 >, < n20 >▷◁< n4 , n5 , n6 , n7 >,3. 2 Determination of Semantic Relationships
Semantic relationships between elements are determined< n3 >▷◁< n19 >, < n5 >▷◁< n19 >, < n20 >▷◁< n19 >}.
3. 2. 2 Semantic Filtersfrom a document tree of a presentation to enable users obtain

We determine semantic relationships between the given

relevant information between the key elements. Preliminary

topics, x and y, from the extracted elements using the set

ideas are given in an algebraic query model [9] as well.

of fragments produced by taking pairwise fragment join as

3. 2. 1 Basic Deﬁnitions and Algebra

semantic ﬁlters. For this, we deﬁne four types of semantic

A presentation content shown in Figure 2 is represented

ﬁlters by considering the horizontal and vertical relevance,Table 1 Semantic relationships with semantic ﬁltersTypes
x shows y
x shows y
x describes yx describes yx likewise yx likewise yx has-a yx has-a yx part-of yx part-of y

Horizontal Vertical

Hierarchy

Inclusion<α>=αeitherl(x) < l(y)either<βl(x) < l(y)either<α>=αeitherl(x) > l(y)either<βl(x) > l(y)either<α>=αeitherl(x) = l(y)either<βl(x) = l(y)either<α>=αeithereither

fx ⊃
= fy

<β

either<α>=αeithereither<βeither

fx ⊃
= fy
fx ⊂
= fy
fx ⊂
= fy

denotes that the level of x is lower than the level of y; x is
a subordinate concept of y (y is a superordinate concept ofx). When l(x) = l(y), the level of x is same as the level of y;
they have coordinate concept.
Inclusion The inclusion relationships exit between the
Figure 3 An example of pairwise fragment join

fragments of x and y. When fx ⊂=fy , the fragment of x is
included in the fragment of y, i.e., fx is a partial tree of fy .

as well as the structural semantics from the document tree.
Horizontal distance When the horizontal distance be-Contrarily, when fx ⊃=fy , it denotes that the fragment of x
includes the fragment of y, i.e., fy is a partial tree of fx .

tween the nodes in slides containing x and y exceeds a cer-

3. 2. 3 Semantic Relationship Types

tain threshold, x is irrelevant to y. Supposing, hdist(ti , tj )We determine ﬁve types of semantic relationships between

denotes the distance between the nodes of the slide titles

the given noun phrases, x and y, by combining the semantic

ti and tj in slides containing x and y, we set the thresholdﬁlters of Table 1. For measuring the relevance between x andvalue α at |N |/2, i.e., half the total number of nodes N iny, we focus on the horizontal distance and the verticalthe document tree, for normalizing various presentations. If

distance. Here, when the horizontal distance between

hdist(ti , tj ) does not exceed α, then the distance between

them is long, the vertical distance should be short. We

two slides containing x and y is near (i.e., relevant); contrar-

determine hierarchical relationships, x shows y, x describes

ily, if hdist(ti , tj ) exceeds α, the distance between two slides

y, and x likewise y, by focusing on hierarchy. In x shows

containing x and y is far (i.e., irrelevant).

y, l(x) < l(y) means x is a superordinate concept of y (y isVertical distance When the distance between the slides

a subordinate concept of x). In x describes y, l(x) > l(y)containing x and y is far, and x and y are at the low lev-means x is a subordinate concept of y (y is a superordinate

els in slides, they can be less relevant in the document tree.

concept of x). Then, show and describe are functionally in-

When vertical distance between the nodes in slides contain-

terchangeable, when x describes y is from the viewpoint ofing x and y exceeds a certain threshold, and x and y are

y shows x. In x likewise y, l(x) = l(y) means x and y haveat the low level in the slides, x is irrelevant to y. Suppos-

coordinate concept with each other. We determine inclusioning, vdist(r, q) denotes the distance between the root node r

relationships, which are x has-a y and x part-of y, by fo-

and the node containing each given noun phrase q (e.g., x orcusing on inclusion. In x has-a y, fx ⊃=fy means that they), we set the threshold value β at ave(depth), which is anaverage of the depth of levels in the document tree, for normalizing various presentations. If vdist(r, q) does not exceed

concept of x includes the concept of y. In x part-of y, fx ⊂=fymeans that the concept of x is included in the concept of y.
Then, has-a and part-of are functionally interchangeable,β, then the level of the node containing x or y is high (i.e.,

when x part-of y is from the viewpoint of y has-a x. When

relevant); contrarily, if vdist(r, q) exceeds β, the level of thex and y fail to match these determinations of semantic re-node containing x or y is low (i.e., irrelevant).

lationships, x and y are independent. Therefore, a numbers

Hierarchy For judging the semantics of x and y, we com-of semantic relationships between x and y are formed frompare the levels of x and y in the fragments based on thea set of fragments produced by taking the pairwise fragment

theory of hierarchical semantics. When l(x) < l(y), the level

join; a semantic relationship is determined by majority.

of x is higher than the level of y; x is a superordinate concept

In this work, we conduct multiple presentations based onof y (y is a subordinate concept of x). Contrarily, l(x) > l(y)the semantic structure analysis in a given domain, the seman-tic relationships follow a transitivity law, e.g., iﬀ x shows ytween the two elements include zooming-out from the focusedin presentation A, y shows z in presentation B, then it iselement to an area enclosing both the elements and theirassumed that x shows z.

parent element, and then zooming-in to the target element.

4. Interactive Poster Generation
We generate an interactive poster possessing two features:(1) providing an overview of elements from the slides, re-

Therefore, the transitions provide their parent element that
can help users easily to know they are subservient to thesame concept.
4. 2. 3 Transitions for part-of (has-a)taining this feature of traditional posters; and (2) utilizing

When has-a (part-of ) between two elements, the transi-a ZUI, promoting user browsing behavior and reﬂecting thetion between the two elements pans from the focused element

semantics of the elements on the interactive posters.

to the target element. Therefore, this simple and direct tran-

4. 1 Determination of Element Layouts

sition between the two elements helps users to easily under-

For providing an overview of elements from slides, we at-stand that they are dependent on each other, and that there

tempt to utilize a hierarchical structure combined with aexists an inclusion relationship between them.

stacked Venn, based on the semantic relationships between

In addition to the above, the transitions between two in-

the elements. When hierarchical relationships between two

dependent elements include zooming-out from the focused

elements, i.e., either show, describe, or likewise exists be-element to all elements, and then zooming-in to the target

tween the elements, they reveal a hierarchy between thoseelement. Therefore, these transitions help the user to easily

elements, as applied to a hierarchical structure. Show orknow that they are irrelevant.

describe maps a parent-child relationship in the hierarchicalAs depicted in Figure 4, we generated interactive posters

structure; if x shows y (y describes x), then we mark x in ausing actual Lecture ♯1 for Database at Stanford Univer-parent area and y in a child area, suggesting that the layersity（注 5） and Portland State University（注 6）. We found that aof x is higher than the layer of y. Additionally, likewise

lecture emphasized the content of ‘DBMS’ at Stanford Uni-

maps a sibling relationship in the hierarchical structure; if

versity, and a lecture emphasized the content of ‘Relational

x likewise y, then we locate x and y in the same layer. In-

Database’ at Portland State University. Then, we conﬁrmed

clusion relationships between two elements, i.e., has-a andthat diﬀerent universities take diﬀerent contents in the same

part-of , reveals a logical relationship of inclusion and exclu-

lecture by grasping overviews of them with our interactive

sion applied, as to a stacked Venn. If x has-a y (y part-ofposters. Then, they can supplement each other, for instance,x), we conceive an area of y that is included in an area of x,

a user wants to obtain details of ‘Relational Database,’ when

and that the area of x is larger than the area of y.

the user browses the lecture at Stanford University.

4. 2 Determination of Element Transitions
To utilize a ZUI, (1) users can browse the interactive
posters with their operations, such as zoom-in, zoom-out,

5. Exploratory Search based on User
Browsing Behavior

and pan; (2) users can browse the interactive posters with-

Based on the the interactive poster generation, we build

out their operations by automatically navigations with tran-

an exploratory search tool that aids users to interactively

sitions between elements. The transitions discussed here ex-search multiple presentations in search results by a given

plain the kinds of visual eﬀects that are applied to the seman-search query. There are two types of exploratory search: (1)tic relationship types, to reﬂect the meaning of the elements

focused searching and (2) exploratory browsing. Therefore,

from the slides. We animate the zooming and panning tran-

we measure dependence of the structure of the interactive

sitions for navigating through elements; this can help users toposter based on user browsing behavior, as follows:

visually understand the overview and details of the contents∑
1
1
D(H) =, en ∈ H|H| − 1dist(en , en+1 )within a presentation.
4. 2. 1 Transitions for show (describe)
When show (describe) between two elements. Then, ﬁrstly

|H|−1

(4)

n=1

Here, H is a browsing history based on user browsing be-

the view must be zoomed-out from the focused element to an

havior. en and em are browsed elements in H. In this work,

area of both, following which; it must be zoomed-in to thewe deﬁne the browsed elements, focusing on zoom-in oper-

target element. Therefore, the transitions include passing

ations of elements by the users, that the elements can be

through the area of both, which helps users to easily grasp

considered as the users are interested in. Then, we calculate

the super-sub relation existing between them.
4. 2. 2 Transitions for likewise（注 5）：http://infolab.stanford.edu/w̃idom/cs145/intro-db.ppt

When likewise between two elements, the transitions be-（注 6）：http://web.cecs.pdx.edu/h̃owe/cs410/lectures/Relational Intro 1.ppt

Figure 4 Examples of generated interactive posters based on our proposed method

a degree of D(H) by using average of relevance between thethe structure of the interactive poster, which helps the userbrowsed elements. Function |H| returns the number of theget details of the last browsed topic from other presentations.

browsed elements. Then, |H|−1 denotes the number of edges

Algorithm 1 describes a procedure for exploring elements in

between the browsed elements. dist(en , en+1 ) is a shortest
distance between en and en+1 in an order. The shortest
distance is calculated by the number of edges between the

Algorithm 2 Explore EB = (Ew , R, P )Require: x, y are elements in a given presentation p, browsed bya user with a zoom-in operation.

browsed elements on the structure of the interactive poster,then, dist(en , en+1 ) >= 1. When dist(en , en+1 ) returns 1, theEnsure: R = {(e, e′ , r)|e, e′ ∈ E, e, e′ ∈ p′ }relevance between en and en+1 is closest. In this case, we set

for x, y such that (x, y, r) ∈ R in p do

the threshold value γ at |H|/|Ep |, |Ep | denotes the numberof nodes in a partial tree included all browsed elements ofthe structure of the interactive poster. If D(H) >= γ, thebrowsing behavior can be considered well-matched on the
structure of the interactive poster; contrarily, if D(H) < γ,
the browsing behavior can be considered distributed on the
structure of the interactive poster.

R⇐ϕfor all presentations p′ in a given domain do
if r is likewise relationship thene⇐xe⇐yr ⇐ describeR ⇐ (x, e′ , r) = (y, e′ , r)
P ⇐ p′
if r is descibe relationship thene′ ⇐ zAlgorithm 1 Explore F S = (Ed , R, P )R ⇐ (e, z, r)

Require: x is an element in a given presentation p, last browsed

P ⇐ p′

by a user with a zoom-in operation.
Ensure: R = {(e, e′ , r)|e, e′ ∈ E, e, e′ ∈ p′ }R⇐ϕfor all presentation p′ in a given domain do

end ifend ifend forend forif r is show relationship thene⇐xR ⇐ (x, e′ , r)
P ⇐ p′a sub-structural layout F S = (Ed , R, P ) from multiple presentations based on topic focused searching. Ed is a set of

end ifelements related to x by users’ operations as an input. r isend fora type of semantic relationships R deﬁned in Table 1. P is aset of presentations in search results by a given query. This

5. 1 Focused Searching based on Well-matched
Browsing Behavior

procedure represents e′ related to x according to (x, e′ , r), in
which r is show for ﬁnding details of x.

When a user browses along the structure of the interac-

Figure 5 illustrates an example of focused searching, a user

tive poster focused on a topic and its subtopics with zoom-in

ﬁrstly zooms-in to the area of ‘Forests and Humans,’ after

operations, we consider the user wants to get details of thethat zooms-out from it and zooms-in to the area of ‘Forests,’

focused topics. In this case, we assume that it is topic fo-

and zooms-in ‘Trees’ on the interactive poster. We consid-

cused searching based on well-matched browsing behavior onered that the user wants to obtain details of ‘Trees’ along

Figure 5 An example of focused searching based on well-matched browsing behavior

Figure 6 An example of exploratory browsing based on distributed browsing behavior

the content related to ‘Forests.’ Therefore, the interactive

shown in ii that gives the user an overview of ‘Trees,’ after

poster represents a whole area of ‘Trees’ with its details (i.e.,that the interactive poster zooms-in to the area of ‘Conifer-‘Coniferous Trees’ and ‘Broadleaved Trees’). Although Pre-ous Trees’ is shown in iii. This enables the user to obtain（注 7）

does not exist details of ‘Trees,’ in this work,‘Coniferous Trees’ is a detail of ‘Trees.’ Next, the interac-

we can extract ‘Coniferous Trees’ and ‘Broadleaved Trees’,

sentation A

tive poster zooms-out from the area of ‘Coniferous Trees’ is（注 8）which describe ‘Trees’ from Presentation B, and repre-

shown in iii to the whole area of ‘Trees’ again is shown insent a whole and represent a whole area of ‘Trees’ with them.

iv, after that the interactive poster zooms-in to the area of

In addition, the interactive poster can automatically navi-‘Broadleaved Trees’ is shown in v. This enables the user togate the whole area of ‘Trees’ to the areas of its details (i.e.,

obtain ‘Broadleaved Trees’ also is a detail of ‘Trees.’‘Coniferous Trees’ and ‘Broadleaved Trees’). As shown in
Figure 5, the interactive poster ﬁrstly zooms-out from thearea of ‘Trees’ is shown in i to the whole area of ‘Trees’ is

5. 2 Exploratory Browsing based on Distributed
Browsing Behavior
When a user browses topics in apart on the interactive
poster, we consider the user wants to get a lot of relevant

information related to the browsed topics.
（注 7）：http://teacherweb.com/AB/GilbertPatersonMiddleSchool/MsDavid/TreeTypes-2b-Posting-version.ppt（注 8）：http://www.marinepolicy.net/cparsons/Ecology/12-Forests.PPT

In this case, we

assume that it is exploratory browsing based on distributed

browsing behavior on the structure of the interactive poster,tions between the elements, based on the semantic relation-

which helps the user ﬁnd a lot of relevant information on

ship types. The interactive poster enables users to browse,

topics of interest from other presentations. Algorithm 2 de-and explore easily and eﬃciently through various presenta-

scribes a procedure for exploring elements in a sub-structural

tions. Finally, we used our collected 25 academic presenta-

layout EB = (Ew , R, P ) from multiple presentations based

tions (392 slides) from DEWS and DEIM in DBSJ Archives,

on exploratory browsing. Ew is a set of elements related to x

and 25 actual lecture contents (432 slides) about social in-and y by users’ operations as an input. This procedure rep-formatics from lecture archives at universities (i.e., Tsukuba′

resents e related to x and y according to (x, y, r), in which

University, Aoyama Gakuin University, etc.) for evaluat-

r is likewise for ﬁnding relevant information of x and y.

ing exploratory searching method based on interactive poster

Figure 6 illustrates an example of exploratory browsing, auser ﬁrstly zooms-in to the area of ‘Forest Ecosystem,’ aftergeneration, such as accuracy of topic extraction (69.1%), validity of overviews and navigation of interactive posters.

that zooms-out it and zooms-in to the area of ‘Food Chain,’In the future, we need to discuss experimental results ofand zooms-out it and zooms-in to ‘Products.’ We consideredinteractive poster generation in detail. Further, we plan tothat the user wants to get a lot of information about ‘Food

consider a collaborative exploratory searching tool, which

Chain’ and ‘Products’ along the content related to ‘Forests

will provide a way to summarize already encountered infor-and Humans.’ Due to ‘Products’ likewise ‘Food Chain,’ andmation. The tool could tailor these summaries to the respec-

they describe ‘Forests and Humans,’ ‘Forests and Humans’

tive skill levels of collaborators.

has its details (i.e., ‘Products’ and ‘Food Chain’) only inpresentation A (see Figure 4). In this work, we can extract

Acknowledgments‘Nitrogen Cycle’ and ‘Rainforest Animals’, which describe

This research was supported in part by Strategic Infor-‘Forests and Humans’ from presentation C, and represent amation and Communications R&D Promotion Programmewhole of ‘Forests and Humans’ with its details (i.e., ‘Prod-(SCOPE) from the Ministry of Internal Aﬀairs and Commu-

ucts,’ ‘Food Chain,’ ‘Nitrogen Cycle,’ and ‘Rainforest Ani-

nications of Japan.

mals’). In addition, the interactive poster can automaticallynavigate the whole area of ‘Forests and Humans’ to the areasof other details (i.e., ‘Nitrogen Cycle’ and ‘Rainforest Animals’). As shown in Figure 6, the interactive poster ﬁrstly
zooms-out from the area of ‘Food Chain’ is shown in i to thewhole area of ‘Forests and Humans’ is shown in ii that gives
the user an overview of ‘Forests and Humans’ with its otherdetails ‘Nitrogen Cycle’ and ‘Rainforest Animals.’ This enables the user to obtain other details ‘Nitrogen Cycle’ and‘Rainforest Animals’ of ‘Forests and Humans.’ Next, the interactive poster zooms-in to ‘Nitrogen Cycle’ and ‘Rainforest
Animals’ are shown in iii and v, respectively. It helps theuser to understand details ‘Nitrogen Cycle’ and ‘Rainforest
Animals’ of ‘Forests and Humans.’

6. Conclusions
In this paper, we built an exploratory search tool forpresentation contents based on interactive poster generation, which represents elements (i.e., textual and graphic elements) in a meaningfully structured layout with automatictransitions, such as zooms and pans, to promote user interaction. Especially, we introduced a semantic structure
analysis model for extracting elements and determining semantic relationships between the elements of slides. In orderto generate an interactive poster, we initially placed the elements in a hierarchical structure combined with a stacked
Venn. We then attached the zooming and panning transi-

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