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Comparing and Managing Multiple

Versions of Slide Presentations

ABSTRACT

Despite the ubiquity of slide presentations, managing mul-

tiple presentations remains a challenge. Understanding how

multiple versions of a presentation are related to one an-

other, assembling new presentations from existing presenta-

tions, and collaborating to create and edit presentations are

difficult tasks. In this paper, we explore techniques for

comparing and managing multiple slide presentations. We

propose a general comparison framework for computing

similarities and differences between slides. Based on this

framework we develop an interactive tool for visually com-

paring multiple presentations. The interactive visualization

facilitates understanding how presentations have evolved

over time. We show how the interactive tool can be used to

assemble new presentations from a collection of older ones

and to merge changes from multiple presentation authors.

ACM Classification: H5.2 [Information interfaces and

presentation]: User Interfaces.- Graphical user interfaces.

Keywords: Slide presentations, versions, distance metrics,

correspondence, alignment

1 INTRODUCTION

Slide presentations have become a ubiquitous means ofsharing information. In 2001, Microsoft estimated that at

least 30 million PowerPoint presentations were created

every day [17]. Knowledge workers often maintain collec-

tions of hundreds of presentations [3]. Moreover, it is

common to create multiple versions of a presentation,

adapting it as necessary to the audience or to other presen-

tation constraints. One version may be designed as a 20

minute conference presentation for researchers, while an-

other version may be designed as an hour long class for un-

dergraduate students. Each version contains different as-

pects of the content.

A common approach to building a new presentation is to

study the collection of older versions and then assemble to-

gether the appropriate pieces from the collection. Similarly,

when collaborating with others on creating a presentation,

the collaborators will often start from a common template,

then separately fill in sections on their own and finally as-

semble the different versions together. Yet, current presen-

tation creation tools [11, 1, 22] provide little support for

working with multiple versions of a presentation simultane-

ously. The result is that assembling a new presentation from

older versions can be very tedious.

In this paper we present new techniques and tools for visu-

ally comparing and managing multiple versions of slide

presentations. Our work makes three main contributions:

Comparison framework: We develop a framework for

comparing presentations to identify the subsets of slides

that are similar across each version and the subsets that dif-

fer. There are a number of ways to measure similarity be-

tween presentations, including pixel-level image differences

between slides, differences between the text on each slide,

etc. We propose several such distance measures and discuss

how they reveal the underlying similarities and differences

between presentations.Interactive visualization: We provide an interactive tool

for viewing multiple versions of a presentation. Users can

examine the differences between presentations along any of

the distance measures computed by our comparison frame-

work. The visualization is designed to help users under-

stand how the presentation has evolved from version to ver-

sion and determine when different portions of it crystallized

into final form. Users can quickly identify sections of the

presentation that changed repeatedly. Such volatility might

indicate problematic areas of the presentation and can help

users understand the work that went into producing the

presentation.

Interactive assembly: Our interactive tool also facilitates

assembly of new presentations from the existing versions.

Users can select subsets of slides from any version and

copy them into a new presentation. The tight integration of

visualization and assembly allows users to see the history of

Steven M. Drucker Georg Petschnigg

Microsoft Research

One Microsoft Way

Redmond, WA 88052, USA

{sdrucker|georgp}@microsoft.com

Maneesh Agrawala

University of California, Berkeley

615 Soda Hall, Mail Code #1776

Berkeley, CA 94720-1776, USA

[email protected]

In submission to UIST 06


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a presentation and combine the most relevant parts into the

new presentation. Such an assembly tool is especially useful

for collaborative production of presentations. Authors can

independently edit the presentation and then use our assem-

bly tool to decide which portions of each version to coa-

lesce into the final presentation.

A screenshot of our interactive visualization and assembly

tool is shown in Figure 1. In this case, the Visual Compari-

son window (Figure 1 left) shows 10 versions of a presenta-

tion. Each column represents a different version. Links and

alignments indicate slides that are similar to one another

from version to version. We discuss our comparison

framework in Section 3 and then show how it is used togenerate the visualization of multiple presentations in Sec-

tion 4. Users can select any subset of slides from the Visual

Comparison window and copy them into the Assembly

window (Figure 1 middle) to create a new presentation. A

yellow border in the Assembly window indicates that sev-

eral slightly different versions of the slide are available. Us-

ers can also select a single slide either in the Visual Com-

parison window or in the Assembly window and an

enlarged version of it appears in the Slide Preview window

(Figure 1 right). The selected slide is highlighted with a

blue border in both the Visual Comparison window and the

Assembly window. We describe the interactive assembly

process in Section 5. We provide examples showing howour system can be used to compare and manage multiple

presentations in Section 6 and conclude in Section 7.

2 RELATED WORK

Finding the similarities and differences between two or

more datasets is a problem that occurs in many contexts.

File differencing tools such as UNIXs diff [9] highlight

line level changes between two documents. These programs

treat files as an ordered sequence of lines and typically

compute the Longest Common Subsequence (LCS) of lines

between two files using dynamic programming techniques

[10, 8]. The LCS algorithm is related to the concept of

string edit distance first introduced by Levenshtein [10].

The string edit distance is defined as the minimum number

of operations (insertions and deletions) required to convert

one string to another. File differencing programs based on

edit-distance are often used by programmers to find all the

lines of codes that were inserted, deleted or changed be-

tween two versions of a file. Similar techniques have been

used to automatically detect plagiarism [16] and to find the

best alignment between genetic sequences [14].

We use string edit distance to help compute distances be-

tween slides (Section 3.2.2), find corresponding slides be-tween presentations (Section 3.3), and align slides in the

visualization (Section 4.1).

Computing differences between data sets solves only part of

the problem. For large data sets it is essential to provide

visualizations that depict the changes and make it easy for

viewers to focus on the similarities and differences between

versions. SeeSoft[4] and SeeSys[5] provide Focus+Context

tools for visualizing differences in text files. Vigas et al.s

[20] have developed the history flow system to visually

compare the changes made to WikiPedia articles. Using his-

tory flow they uncover a variety of patterns of cooperation

and conflict that arose naturally as authors collectively cre-

ated and edited WikiPedia. Their system is aimed at visual-

izing hundreds of versions of text documents. While we

draw on this work for inspiration, our work is aimed at

comparing slide presentations that contain graphics, images,

and text. Unlike the earlier systems we also provide tools

for assembling new presentations from older version.

Slide presentation tools such as PowerPoint[11] and Key-

note [1] usually focus on providing tools for creating and

presenting a sequence of slides. While PowerPoint does

provide a track change mode for merging changes be-

Visual Comparison Window Presentation Assembly Window Slide Preview WindowFigure 1: Our interactive visualization and assembly tool is comprised of a Visual Comparison window (left), an Presen-tation Assembly window (middle) and a Slide Preview window (right). Users examine multiple presentations (each col-umn of the Visual Comparison window shows a different presentation) and find the similarities and differences betweenthem. Users can select any subset of slides from the Visual Comparison window and assembly them into a new presen-tation. The Single Slide window allows users to inspect one slide and its alternate versions in greater detail.


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tween two presentations, it forces a specific workflow. Us-

ers must process slides one at a time and accept or reject

changes. PowerPoint does not provide any way of seeing an

overview of all the differences between multiple presenta-

tions at once. Our system provides this overview and allows

users to work with multiple presentations simultaneously.

While current commercial slide creation software focuses

on producing a single linear sequence of slides, several re-

search systems support multiple paths though a presenta-

tion. Pad[18] and CounterPoint [6] are zoomable interfaces

that allow spatially positioning slides on an infinite canvas

and support hyperlinked navigation to any slide in the pres-

entation. Zellweger [0] has developed a system for building

multimedia documents embedded with multiple scripted

paths. Nelson et al.s [15] Pallete system is a tangible, pa-

per-based interface for organizing presentations. A physical

card is used to represent each slide and users can easily re-

organize the slide sequence by rearranging the physical

proxies. More recently, Moscovich et al. [13] have devel-

oped a system that allows users to choose between multiple

paths, on-the-fly, as they are giving the talk. All of these

systems facilitate the process of customizing a presentation.

Our system is aimed at comparing and managing multiple

presentations, therefore, it is largely orthogonal to these

techniques and could be used in conjunction with any of

them.

3 COMPARISON FRAMEWORK

The goal of comparing two slide presentations is to identify

all of the similarities and differences between the slides

comprising each presentation. The key step is to find for

each slide in the first presentation the best matching slide

in the second presentation, yet we can compute such match-

ing correspondences with respect to many different features

of slides. Moreover, the best correspondence in one featuremay not be the best in another feature. Therefore we have

developed a general framework for computing such corre-

spondences with respect to a variety of features.

Figure 2: Slide features currently used in our com-parison framework.

For each slide in a presentation, we extract a set of basic

features (discussed in Section 3.1) and then use feature-

specific distance operators (Section 3.2) to compute a set

of distances between pairs of slides one distance per fea-

ture type. Next we apply correspondence operators (Sec-

tion 3.3) to find the best match between a slide in the first

presentation and a slide in the second presentation.

3.1 Slide Features

We consider any basic descriptive element of a slide to be a

feature. The graphical elements including vector drawings,

images, charts and tables, as well as the text contained on a

slide are all examples of slide features. We also consider

the bitmap image of a slide to be a feature of it. Other ex-amples of slide features include the position of text boxes

and graphic elements, background graphics or colors, for-

matting parameters of text, header text, footer text, note

text, and animation settings. Some features are specific to

the tool used to create the presentation. For example,

PowerPoint assigns a unique ID for each slide and for each

image on a slide. For a comprehensive list of object model

level features see the file format specifications of Microsoft

PowerPoint [12] or Apples Keynote [1].

Figure 2 describes the features that we use in our frame-

work. Although our implementation currently includes only

a few basic features that we have found most useful for

comparing presentations, the framework could easily be ex-tended to handle any descriptive feature of a slide.

3.2 Distance Operators

The first step in comparing two presentations is to compute

distances between the slides with respect to the underlying

slide features. Each distance operator takes two presenta-

tions and computes a distance for each pair of slides the

first slide is from the first presentation, and the second slide

from the second presentation. We have implemented sev-

eral different distance operators that measure how the text

and images differ between slides. These distances are the

basic building block for comparing presentations.

3.2.1 Image based distanceWe compute the visual distance between two slides by cal-

culating the mean square error (MSE) between their bitmap

images. The MSE measures visual similarity and a MSE of

zero means that the two slides are visually identical to one

another. Thus, a small MSE implies that slides are visually

very similar to one another, while a large MSR implies that

there may be large visual differences between the slides.

A drawback of MSE, is that it often does not match human

perceptions of visual differences. For example, slightly

changing the position of an image between two slides can

produce a large MSE, even though the slides will look very

similar. Similarly, a minor insertion or deletion of text that

causes the text to reflow will produce a relatively largeMSE. Yet, the meaning of the text may not have changed at

all. Alternate image comparison based on sub-region com-

parison of the image may be less sensitive to small object

transformations. Similarly image distance metrics based on

models of human visual perception might provide more

meaningful distances. Nevertheless we have found MSE to

be a very useful measure of slide similarity and it is par-

ticularly useful for identifying visually identical slides.


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3.2.2 Text distance (Levenshtein or edit distance)

As mentioned previously, the string edit distance measures

the minimum number of operations required to convert one

string into another string. Our text distance operator uses

Levenshteins dynamic programming algorithm [10] to effi-

ciently compute the edit distance between textual features

(Slide Title, Body Text). The basic algorithm is to build a

matrix of costs required to convert one string into another;

the costs are based on inserting a character in one sequenceor in the other.

Another approach to compare text strings is based on a tri-

gram model [19]. The idea is to build a histogram of all

three letter sequences of characters within each string. The

distance between the strings is then computed as the dot

product of the histograms. The advantage of this approach

is that it is less sensitive than string edit distance to rear-

rangements of text. For example, reordering bullet points

in the body text of a slide will yield a large string edit dis-

tance but a relatively low trigram distance. In practice,

however we have found that the string edit distance pro-

vides a good measure of text similarity.

3.2.3 Comparison of slideIDs, picture IDs

Slide IDs and Picture IDs are PowerPoint specific features.

They are unique identifiers for each slide and each image

on a slide and once created they remain fixed for the life-

time of a document. Thus, we can directly compare these

IDs to identify matching slides and images between two

versions of a presentation. The Slide ID distance operator

returns 0 if the slide IDs match and a very large value when

they do not match. The Picture ID distance operator deter-

mines the maximum number of images in common between

the two slides and returns the reciprocal of that number plus

1, thus slides with many matches have lower distances than

those slides with fewer or no matches.While a Slide ID distance of 0 shows that two slides once

started out as identical, there is no guarantee that the slides

remain similar. The slides could have been heavily edited

within each presentation independently. Similarly even if

slide IDs differ, the slides may be visually identical. as the

simple act of copy/pasting (as opposed to cut/paste) will

produce identical slides with different Slide IDs. Yet the

Slide ID distance does provide a notion of slide similarity

that is insensitive to subsequent slide edits.

3.3 Slide Correspondence Operators

To find the best match between slides in each presentation

we compute slide to slide correspondences. These corre-

spondences are the key to identifying the changes between

presentations. As we will show in Section 4 our interactive

visualization tool is designed to visually depict these corre-

spondences so that users can quickly see similarities and

differences between multiple presentations.

Correspondence operators take two presentations as input,

and yield a mapping between each slide in the first presen-

tation and its best matching slide in the second presentation.

In our implementation, each slide can appear in at most one

match, and if no good match is found the correspondence

operator can leave a slide unmatched. Correspondences are

computed based on the distances between slides.

3.3.1 Minimum Distance Correspondence

A simple technique for computing correspondence is as fol-

lows. For each slide in the first presentation, find the mini-

mum distance slide in the second presentation. While this

approach could be used in conjunction with any of our dis-

tance operators, it has several drawbacks. If multiple slides

are at the same minimum distance, it is unclear how to pick

the best match from amongst them. There is no provision

for leaving a slide unmatched; even if none of the slides in

the second presentation is a good match, this technique will

still generate a correspondence. Many slides might match to

the same slide, and finally the technique is asymmetric as

matching the first presentation to the second will produce

different results than matching the second to the first.

3.3.2 Longest Common Subsequence

Another option for finding correspondences is to use a

Longest Common Subsequence (LCS) algorithm [10] to

find the longest sequences of slides that match from one

presentation to the other. However LCS does not findmoved blocks, but rather, uses symbol insertions and dele-

tions which prevents correspondences from being created

that cross each other (eg. a group of slides is moved from

later in a presentation to earlier in a subsequent presentation

while a group in the middle stays the same).

3.3.3 Greedy-thresholded correspondence

Heckel [7] uses a greedy algorithm which finds uniquely

corresponding symbols, removes them from the potential

set for consideration, and then expands the search from

those symbols to adjacent symbols in order to find the best

correspondences. This algorithm iterates until no more

matches are found.

Since were using feature distances instead of exact matches

between symbols, this algorithm does not quite work for

our situation. We use a similar greedy algorithm, which

contains a threshold so that slides that are more than a

minimum distance away are never matched with other

slides. We start with the minimum distances between slides.

The algorithm is as follows:

1. Slide distances for a feature are sorted from leastto greatest.

2. For each slide in each presentation, find the slidewith minimum distance subject to a minimum

threshold distance.

3. Create a new correspondence between these slides

4. Remove both slides from potential subsequent cor-respondences.

5. Continue until no more correspondences can befound.

This version works reasonably, but can still run into some

problems. Its not clear whether to assign a greater priority

to neighboring slides with slightly greater feature distances

versus lower feature distant, but further slides. Also, any


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greedy algorithm might not come up with an overall best

solution for all slides involved.

3.3.4 Composite correspondences

It is often convenient to create correspondences from sev-

eral different distances at the same time since the system

can only align on one correspondence at a time. For in-

stance, by using both image and text distances, we can cre-

ate a single correspondence that works well for both slides

with extensive amounts of text and those with no text, butonly images. There is some heuristic tuning that needs to be

done when combining these two different distances since

the image distances are in the number of different pixels be-

tween the slide images, and the text is in the number of in-

sertions and deletions required to convert one text string to

another. We simply use the correspondence with the mini-

mum distance (after normalizing the text and image dis-

tances).

By adding additional feature distances, such as slide ID, we

can arbitrate when the other measures produce different

correspondences. Specifically, if neither text nor image dis-

tance yield an exact match and both text and image dis-

tances result in a different correspondence, the slide ID cor-

respondence is used if it is the same as one of the others. If

none agree, then no correspondence is produced.

4 VISUALIZING MULTIPLE PRESENTATIONS

To help users understand similarities and differences in the

presentations, we generate a visualization that reveals cor-

respondences between presentations and lets users interact

with it in a variety of ways.

In the version pictured in Figure 3, each rectangle repre-

sents a single slide and slide presentations are represented

in columns. In the initial layout (Figure 3-a), the relative

lengths of both presentations is immediately apparent.

4.1 Conveying correspondence

We can connect corresponding slides with lines which use

color to convey the type of the correspondence. Corre-

sponding slides measured along any feature can also be

aligned. The visualization computes a minimum number ofgaps to maximize alignment of corresponding slides be-

tween two presentations given the constraint that each pres-

entation must not modify the order in which the slides occur

(Figure 3-c).

We again use a string alignment algorithm, based on

Levenshtein to compute optimal alignment. In this case, we

use a modified Hirschberg [8] implementation which uses

less space then a standard Levenshtein string matching al-

gorithm. Instead of matching string characters, we base a

match on the chosen correspondence function and use it to

build up our cost matrix of insertions and deletions. If two

slides correspond, then a cost of 0 is added to the matrix.Otherwise, a cost of 1 is used in each of the directions indi-

cating insertion in either sequence. After the minimum cost

has been determined, this same matrix can be used to de-

termine maximal alignment by backtracking through the

matrix and following where insertions have been made.

As more presentations are added to the comparison, gaps

are adjusted throughout all the presentations to keep corre-

sponding slides aligned when possible. In order to do so,

we move through the presentations from earliest to latest

computing alignments gaps for each presentation. Gaps

v 1 v 2 v 1 v 2 v 1 v 2 v 1 v 2 v 3

(a) (b) (c) (d) (e)

v 1 v 2 v 3

Figure 3: Given two presentations (a) our visualization can show various slide correspondence types (b). Maxiumumalignment is achieved using a Hirschberg string matching algorithm (c) which inserts a minimum number of gaps evenwith multiple slide versions (d). An alternate layout comparing one to many presentations in shown in (e).


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must be inserted throughout all the already aligned presen-

tation to keep them aligned (indicated in Figure 4 by the red

ellipses).

No alignment 1 - 2 aligned 1 -2 - 3 aligned 1 - 2 - 3 - 4 aligned

Figure 4: Alignment of multiple presentations

Weve also found it useful to distinguish between slides that

are exact matches and those that are just correspond. For

example, a text edit distance of 0 may indicate that slides

text correspond identically, but does not include formattingor positioning on the page. We convey the notion of exact

and inexact matches using colored end caps at the end of

the corresponding lines. Different distance measures can be

used to color the end caps but in practice, we use the visual

distances since exact matches in the image means that the

slides will be visually indistinguishable. We can turn this on

and off with a button in the interface. These caps can be

seen in Figures 1, 6, and 7.

Finally, we can dim slides that do not change at all from

one version to another to help emphasize those that do

change. An example of this can be seen in Figure 6 in the

results section.4.2 Presentation to presentation visualizations

Sequential, one-to-one, comparisons are useful for tracking

changes on a single presentation over multiple versions.

Some slides have no correspondences between next or pre-

vious presentations (either because they have been newly

introduced in a subsequent presentation, deleted from a

previous presentation; or modified enough that no corre-

sponding slide can be found). This is shown in the visuali-

zation as a single slide at the beginning (for newly intro-

duced slides) or end of a row (for deleted slides). Slides

that have been moved across stable boundaries cannot be

aligned, but are still connected by corresponding links. An

example is pictured in Figure 6 of the results section.

The one-to-many comparisons are useful in examining dif-

ferences between one base presentation and alternative ver-

sions. This may be because the version has been used to as-

sembly new presentations or because multiple collaborators

are simultaneously working on alternate presentations. Each

slide is connected (and potentially aligned with) a corre-

sponding slide in the first presentation. These are shown in

Figures 5 and 7 of the results section.

4.3 Interacting with the visualization

The user can interact with the visualization by using a slider

to zoom out to see an overview of the changes, or to zoom

into a particular slide or region of slides. Clicking on a slide

will select it and bring up a full resolution slide in a slide

preview window. The user can use the arrow keys on the

keyboard to move the selection forward or backward within

a presentation, or move between corresponding slides

within presentations. By quickly moving back and forth be-tween corresponding slides, the user can easily perceive dif-

ferences in the slides using the preview window.

Checkboxes allow different correspondence links to be

turned on and off, and a pull down menu allows the presen-

tations to be aligned along any of the correspondences. Im-

ages of slides can be turned on or off to just focus on the

overall structure of changes. The user can also changed the

layout to horizontal or vertical depending on the preferred

mode of operation.

5 ASSEMBLING PRESENTATIONS

Besides allowing analysis of the relationships between mul-

tiple presentations, the visualization tools also facilitate theassembly of new presentations. We designed these tools in

to support common usage patterns among presenters. Users

often pull from a large number of related presentations in

the creation of a new presentation. They also often work

with collaborators and need ways to examine and incorpo-

rate differences into a single presentation.

Users can select slides from the visualization in a number of

ways: individual slides can be selected by clicking on the

slides themselves; all the slides within a presentation can be

selected by clicking on the presentation title; all slides that

have a particular term can be selected by searching for

them; and finally, all changed slides can be easily selected

using a button in the interface. Users can also move to thenext change (as indicated by a slide with no correspon-

dence, or a corresponding slide with visual differences) de-

tected in any presentation.

Selected slides can then be inserted into a newly created

presentation at the current selection point. Slides can be re-

arranged within the new presentation via drag and drop or

standard cut and paste. The slides also still maintain their

correspondences to slides in the other presentations, and the

user can easily choose with the arrow keys between alter-

nate slides (relative to different correspondences) in the

newly created presentation. Slides that have visually distin-

guishable correspondences are colored yellow to indicate

that alternates are available.

Strategies for assembling presentations can include starting

with all the slides in the first version, copying them into the

new presentation and then deciding which changed slides to

use. Alternatively, the user can start with a final version and

choose which changes to roll back. The user can also

choose individual slides or slide ranges from the existing

presentations and insert them into the newly created presen-

tation.


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Users can then save the new presentation and edit it within

PowerPoint or some other slide creation program.

6 IMPLEMENTATION

The system was implemented using the Microsoft Office

Primary Interop Assemblies to access the object model for

PowerPoint and automate the extraction of all the features

contained on the slides including the text, IDs, and the im-

ages. The visualization was programmed using the Win-dows Presentation Framework, and a variant of Python

called IronPython which uses the Common Language Run-

time (CLR) which facilitated rapid development and al-

lowed for convenient loading of modules for visual com-

parison, textual comparison, and PowerPoint interaction.

The code is not currently optimized and takes approxi-

mately 1 minute to extract features and compare two mod-

erate sized presentations on 2 GHz P4 computer with 1 Gb

of RAM. The features and the comparisons are saved in

XML files so that once run, the comparison will only re-run

if the source presentations are altered.

7 RESULTS

Our results are depicted in Figures 5 7. Figure 6 shows avisualization of 10 different versions of a presentation pre-

pared by multiple authors for an executive review. The

visualization totals 497 slides. In this view, identical slides

have been dimmed to draw attention to 112 slides that have

been edited. Each version of the presentation is sequentially

compared to the next which allows for an analysis of the

presentation over time. In version 3 and in version 8 several

slides have been added as indicated by the large insertion

gaps. Conversely from version 5 to version 6, a six slide

section was removed to shorten the presentation. Slide

changes occur all the way to the end, across the entire pres-

entation reflecting modifications introduced after rehearsing

the presentations.

Figure 5 shows a one-to-many comparison where several

authors edited a single base presentation and the system is

used to identify and coalesce changes. The system shows

when authors spot the same typo or how different authors

might suggest alternate changes to the flow of the presenta-

tion.

Figure 7 depicts a case of presentation assembly. Here a re-

searcher prepares for a mid year review by pulling slides

from two research talks given earlier in the year. Our visu-

alization lets the researcher compare the two presentations

Figure 7-a and choose the desired slides (outlined in or-

ange). For example the second slide in the assembly is fromversion 2, the fifth slide from version 1. Additionally the

visualization uses gaps to show which slides only exist in

one version. Once the assembly step is complete, the re-

searcher can save out a new version of the presentation and

make modifications such as updating the title slide. Figure

7-b uses our one-to-many correspondence. Here the newly

assembled presentation is compared to its sources. This

view shows from which presentation slides were taken. A

line without red dots indicated an identical image match,

which is a strong correspondence. In this view the re-

searcher can still swap out slides with their alternate ver-

sions.

8 CONCLUSIONS

We have presented a framework and set of visualization

tools for analyzing and simultaneously presenting multiple

presentations.

These tools can be used to assist in the creation of new

presentations and support a variety of work strategies fromtracking changes for individuals, merging multiple versions,

or assembling new presentations.

Looking forward, the creation process of presentations has

not received much study, despite their popularity. Our visu-

alization can give sociologists the tools to detect patterns in

multiple versions of a slide presentation or even among all

the presentations owned by a user or organization. For ex-

ample the size of a presentation over time can be studied as

well as the time when a presentation crystallizes. We be-

lieve our tool could be helpful in analyzing the creative

journey a presenter takes in preparing a presentation since

corresponding slides, the main artifact in a presentation, can

be tracked over time.

v 1 v 2 v 3 v 4

authors v2 and v4 spot

the same typo

author v4 changes the

slide

v3 and v4 propose

moving these slides,howeverto different

locations

v4 proposes to moveand changes slides

which would cause a

new narrative flow for

this section

contact slides at theend of talks usually do

not change much

Figure 5: Merging changes using a one-to-manycomparison.


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v1 v2 v3 v4 v5 v6 v7 v8 v9 v10

from v5 to v6 this

section is removed to

shorten the talk

the talk is rehearsed

from v7 through v10which causes severalchanges

at v3 another author

adds slides

at v2 the second

author adds slides

from v6 to v7 a

slide is moved and

expanded upon

v1 is the basepresentation for an

executive review

Figure 6:Ten versions of a presentation prepared for a review.The presentation consists of 487 slides. Identical slides are

dimmed to bring focus to the 112 changed slides. Gaps denote

were sections where added and removed. For example between

v5 and v6 a large section was removed to shorten the talk.


9/9

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(b)

v 1 v 1v 2 v 2Assembled v 3

v 3

(a)

Figure 7: (a) Our system as used for presentation assembly. V 1 and V 2 are two related research presentations. Thesequential comparison makes it easy to choose slides from the two versions: Alternate versions of a slide are aligned,and slides that have changed under the image metric are denoted with red dots. The user can pick the desired slides(shown in orange) and add them to the presentation assembly. In (b) the assembled presentation is compared to itssource versions. Our one-to-many comparison shows from which version of a presentation a slide came from and ifan alternate slide exists.

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