pptdiffuist
Post on 04-Apr-2018
216 Views
Preview:
TRANSCRIPT
-
7/29/2019 pptdiffuist
1/9
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
maneesh@cs.berkeley.edu
In submission to UIST 06
-
7/29/2019 pptdiffuist
2/9
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.
-
7/29/2019 pptdiffuist
3/9
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.
-
7/29/2019 pptdiffuist
4/9
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
-
7/29/2019 pptdiffuist
5/9
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).
-
7/29/2019 pptdiffuist
6/9
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.
-
7/29/2019 pptdiffuist
7/9
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.
-
7/29/2019 pptdiffuist
8/9
9 REFERENCES1. Apple. Keynote. 2005.
http://www.apple.com/keynote; Keynote Document Ref :
http://developer.apple.com/technotes/tn2002/tn2073.html
2. Baker, M.J., and Eick, S. 1995. Space-filling software visuali-zation. Journal of Visual Language and Comuting. 6. pp.
119133.
3. Brand, John. 2004. Presentation (Mis)management: Contentand Collaboration Strategies, Delta 3057. Sept 14, 2004.
4. Eick, S., Steffen, J.L., and Sumner, E.E. 1992. Seesoft Atool for visualizing line oriented software statistics. IEEE
Transactions on Sotware Engineering. 18, 11. pp. 957968.
5. Eick, S. 1994. Graphically displaying text.Journal of Compu-tational and Graphical Statistics. 3, 2. pp. 127142.
6. Good, L., and Bederson, B. 2002. Zoomable user interfaces asa medium for slide show presentations.Information Visualiza-
tion. 1, 1. pp. 3549.
7. Heckel, P. 1978. A technique for isolating differences betweenfiles. Communications of the. ACM21, 4. pp. 264268.
8. Hirschberg, D.S. 1975. A linear space algorithm for comput-ing maximal common subsequences. Communications of theACM. 18, 6. pp.341343.
9. Hunt, J.W., and McIlroy, M.D. 1976. An algorithm for differ-ential file comparison. Bell Telephone Laboratories CSTR
#41.
10.Levenshtein, V.I.. 1966. Binary codes capable of correctingdeletions, insertions and reversals. Soviet Physics Doklady..
pp 707710.
11.Microsoft. PowerPoint. 2003.http://www.microsoft.com/powerpoint
12.Microsoft PowerPoint Object Model Reference,http://msdn.microsoft.com/office/understanding/powerpoint/
13.Moscovich, T., Scholz, K., Hughes, J.F., and Salesin, D.2004. Customizable presentations. Technical Report CS-04-
16, Computer Science Department, Brown University.
14.Needleman, S. and Wunsch, C. 1970. A general method ap-plicable to the search for similarities in the amino acid se-
quence of two proteins, J Mol Biol. 48(3):443-53
15.Nelson, L., Ichimura, S., Pedersen, E.R., and Adams, L. 1999.Palette: A paper interface for giving presentations. In Pro-
ceedings of CHI 1999. pp.354361.
16.Parker, A., and Hamblen, J.O. 1989. Computer algorithms forplagiarism detection. IEEE Transactions on Education. 32, 2.
pp. 94-99.
17. Parker, I. 2001. Absolute PowerPoint: Can a software pack-age edit our thoughts? The NewYorker. pp. 7687.
18.Perlin, K., and Fox, D. 1993. Pad: An alternative approach tothe computer interface. In Proceedings of SIGGRAPH 2003.
pp. 5764.
19. Salton, G. and McGill, M. J. 1986. Introduction to ModernInformation Retrieval. McGraw-Hill, Inc.
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.
-
7/29/2019 pptdiffuist
9/9
20.Vigas, F. B., Wattenberg, M., and Dave, K. 2004. Studyingcooperation and conflict between authors with history flow
visualizations. In Proceedings of CHI 2004. pp. 575-582.
21. Zellweger, P.T. 1989. Scripted documents: A hypermediapath mechanism. In Proceedings of ACM Hypertext 1989.
22.Zongker, D. E. and Salesin, D. H. 2003. On creating animatedpresentations. In Proceedings of the 2003 ACM Sig-
graph/Eurographics Symposium on Computer Animation. pp.
298-3
(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.
top related