Decision Support Systems 43 (2007) 46 – 61
www.elsevier.com/locate/dss
An information delivery system with automatic summarization
for mobile commerce
Christopher C. Yang a,*, Fu Lee Wang b
a
Department of Systems Engineering and Engineering Management, The Chinese University of Hong Kong, Shatin, Hong Kong, China
b
Department of Computer Science, City University of Hong Kong, Kowloon Tong, Hong Kong, China
Available online 14 July 2005
Abstract
Wireless access with handheld devices is a promising addition to the WWW and traditional electronic business. Handheld
devices provide convenience and portable access to the huge information space on the Internet without requiring users to be
stationary with network connection. Many customer-centered m-services applications have been developed. The mobile
computing, however, should be extended to decision support in an organization. There is a desire of accessing most update
and accurate information on handheld devices for fast decision making in an organization. Unfortunately, loading and
visualizing large documents on handheld devices are impossible due to their shortcomings. In this paper, we introduce the
fractal summarization model for document summarization on handheld devices. Fractal summarization is developed based on
the fractal theory. It generates a brief skeleton of summary at the first stage, and the details of the summary on different levels of
the document are generated on demands of users. Such interactive summarization reduces the computation load in comparing
with the generation of the entire summary in one batch by the traditional automatic summarization, which is ideal for wireless
access. The three-tier architecture with the middle-tier conducting the major computation is also discussed. Visualization of
summary on handheld devices is also investigated. The automatic summarization, the three-tier architecture, and the information
visualization are potential solutions to the existing problems in information delivery to handheld devices for mobile commerce.
D 2005 Elsevier B.V. All rights reserved.
Keywords: Document summarization; Mobile commerce; Fractal summarization; Handheld devices; Financial news delivery
1. Introduction
The advance of mobile network creates business
opportunities and provides value-added services to
users. Access to the Internet through mobile phones
* Corresponding author. Tel.: +852 2609 8239; fax: +852 2603
5505.
E-mail address: [email protected] (C.C. Yang).
0167-9236/$ - see front matter D 2005 Elsevier B.V. All rights reserved.
doi:10.1016/j.dss.2005.05.012
and other handheld devices is growing significantly in
recent years. Many m-services applications have been
developed for the handheld devices [5–8,38]. However, most current applications are customer-centered
applications, for example, users can now surf the web,
check e-mail, read news, and quote stock price, etc.,
using handheld devices. The mobile computing
should not be limited to user-centered applications
only. In this age of information, mobile applications
C.C. Yang, F.L. Wang / Decision Support Systems 43 (2007) 46–61
should be extended to decision support in an organization. With a fast paced economy, organizations need
to make decisions as fast as possible, they must gain
competitive advantage by having access to the most
current and accurate information available. For
instance, a huge amount of financial news is generated
everyday, and access to update financial information
is important during decision making. On the other
hand, the executives of an organization need make
decision when they are on the road. As a result, there
is an urgent need of information access through handheld devices.
There are many shortcomings associated with
handheld devices although the development of handheld devices is fast in the recent years. The shortcomings include limited screen size with low
resolution, low bandwidth, and low memory capacity.
It is impossible to search and visualize the critical
information on a small screen with an intolerable slow
downloading speed using handheld devices. Automatic summarization summarizes a document for
users to preview its major content. Users may determine if the information fits their needs by reading the
summary instead of browsing the whole document
one by one. The amount of information displayed
and downloading time are significantly reduced.
Active researches on automatic summarization have
been carried out. Summarization techniques have been
applied to delivery of information on handheld
devices [3,42]. Traditional summarizations do not
consider the hierarchical structure of document but
consider the document as a sequence of sentences.
Most traditional summarization systems extract sentences from the source document and concatenate
together as summary. However, it is believed that
the document summarization on handheld devices
must make use of btree viewQ [7] or bhierarchical
displayQ [32]. Similar techniques have been applied
to web browsing, an outline processor organizes the
web page in a tree structure and the user click the link
to expand the subsection and view the detail [4].
Hierarchical display is suitable for navigation of a
large document and it is ideal for small area display.
Therefore, a new summarization model with hierarchical display is required for summarization on handheld
devices. Summarization of web pages on handheld
devices has been investigated [5–8]. However, a
large document exhibits totally different characteris-
47
tics from web pages. A web page usually contains a
small number of sentences that are organized into
paragraphs, but a large document contains much
more sentences that are organized into a more complex hierarchical structure [48,49]. Besides, the summarization on webpage is mainly based on thematic
features only [6]. However, it has been proved that
other document features play a role as important as the
thematic feature [10,22]. Therefore, a more advance
summarization model combined with other document
features is required for browsing of large document
and other information sources on handheld devices
[49–51]. With powerful summarization tool, the ability of handheld devices will be greatly enhanced.
Visualization of various information sources becomes
feasible, for example, financial news delivery to handheld devices. It provides an information visualization
tool for m-commerce.
The information access through mobile devices has
drawn attention from researchers in recent years.
Mobile devices support instant information and data
access that is capable to support decision making. For
example, Mendelsohn [35] has presented how physicians may request information to answer questions
that occur at the point-of-care through mobile devices.
A number of researchers have worked on advanced
text input entry for handheld device [29,30,39]. More
specifically, there are researches that focus on navigation of documents on mobile devices. Lamming et al.
[26] have presented the Satchel system that used
tokens to represent documents on mobile device and
developed a context-sensitive user interface. In this
work, we focus on the delivery of information to
handheld devices with the support of automatic summarization. Example of how the proposed system
supports the delivery of Yahoo! financial information
is presented.
The paper is organized as follows. Section 2 discusses the shortcomings of handheld devices and
information visualization on handheld devices. The
three-tier architecture, which reduces the computing
load of the handheld devices, is used. Section 3
proposes the fractal summarization model based on
the statistical data and the hierarchical structure of
documents. Thematic, location, heading and cue features are adopted. Experiments have been conducted
and the results show that the fractal summarization
outperforms the traditional summarization. Fractal
C.C. Yang, F.L. Wang / Decision Support Systems 43 (2007) 46–61
Web Server
HTML
XML
Document
Server
2. Document delivery architecture on handheld
devices
Traditionally, two-tier architecture is typically utilized for Internet access. The user’s PC connects to the
Internet directly, and the content loaded will be fed to
the web browser and present to the user as illustrated
in Fig. 1.
However the information available online has
increased explosively, it results in a well-recognized
problem of information overloading. Advance-searching techniques solve the problem by filtering most of
the irrelevant information. However, the precision of
most of the commercial search engines is not high.
Users may find only a few relevant documents out of
a large pool of searching result. Due to the huge
volume of documents, it will take a lot of time for
the users to browse the searching result one by one
and identify the relevant information using desktop
computers. Automatic summarization summarizes a
document for users to preview its major content.
Users may determine if the information fits their
needs by reading their summary instead of browsing
the whole document one by one. The amount of
information displayed and downloading time are significantly reduced. An automatic summarizer is therefore introduced to summarize a document for the users
to preview before presenting the whole document. As
shown in Fig. 2, the content will be first fed to the
summarizer after loading to the user’s PC. The summarizer connects to database server when necessary
and generates a summary to display on the browser.
The convenience of handheld devices allows information access without geometric limitation; however,
SQL
DB Server for
Summarizer
User’s PC
there are other limitations of handheld devices that
restrict their capability. Although the development of
wireless handheld devices is fast in recent years, there
are many shortcomings associated with these devices,
such as screen size, bandwidth, and memory capacity.
There are two major categories of wireless handheld
devices, namely WAP-enabled mobile phones and
wireless PDAs. At present, the typical display size
of popular WAP-enabled handsets and PDAs are relatively small in comparison with a standard PC. The
memory capacity of a handheld device greatly limits
the amount of information that can be stored. A large
document cannot be entirely downloaded to the handheld device and present to user directly. The current
bandwidth available for WAP is relatively narrow. It is
not comparable with the broadband internet connection for PC. The handheld devices impose other constraints that do not exist on desktop computers. The
two-tier architecture cannot be applied on handheld
devices since the computing power of handheld
devices is insufficient to perform summarization and
the network connection of mobile network does not
provide sufficient bandwidth for navigation between
the summarizer and other servers.
The three-tier architecture as illustrated in Fig. 3 is
proposed. A WAP gateway is setup to conduct the
summarization. The WAP gateway connects to InterHTML
Fig. 1. Document browsing on PC.
SQ
DB Server for
Summarizer
Browser
Browser
User’s PC
Document
Server
Summarizer
WML
XML
User
User
Fig. 2. Document browsing with summarizer on PC.
Web Server
Web Server
Browser
summarization is further extended to information
browsing on handheld devices. Section 4 will demonstrate the financial news delivery on handheld devices.
Summarizer
48
Wireless
Handhel
PDA
Local
Synchronization
WAP Gateway
Fig. 3. Document browsing with summarizer on WAP.
User
C.C. Yang, F.L. Wang / Decision Support Systems 43 (2007) 46–61
net trough broadband network. The wireless handheld
devices can conduct interactive navigation with the
gateway through wireless network to retrieve the
summary piece by piece. Alternatively, if the PDA
is equipped with more memory, the complete summary can be downloaded to PDA through local
synchronization.
3. Automatic summarization
3.1. Traditional summarization
Traditional automatic text summarization is the
selection of sentences from the source document
based on their significance to the document [10,28].
The selection of sentences is conducted based on the
salient features of the document. The thematic, location, heading, and cue features are the most widely
used summarization features.
! The thematic feature is first identified by Luhn
[28]. Edmundson proposed to assign the thematic
weight to keyword based on term frequency, and
the sentence thematic score as the sum of thematic
weight of constituent keywords [10]. In information retrieval, absolute term frequency by itself is
considered as less useful than term frequency normalized to the document length and term frequency
in the collection [18]. As a result, the tfidf (Term
Frequency, Inverse Document Frequency) method
is proposed to calculate the thematic weight of
keyword [41].
! The significance of sentence is indicated by its
location [2] based on the hypotheses that topic
sentences tend to occur at the beginning or in the
end of documents or paragraphs [10]. Edmundson
proposed to assign positive weights to sentences
as sentence location score according to their
ordinal position in the document, i.e., the sentences in the first and last paragraphs and the first
and last sentences of the paragraphs. There are
several functions proposed to calculate the location weight of sentences. Alternatively, the preference of sentence location can be stored in a
list called Optimum Position Policy, and the sentences will be selected base on their order in the
list [27].
49
! The heading feature is proposed based on the
hypothesis that the author conceives the heading
as circumscribing the subject matter of the document. When the author partitions the document into
major sections, he summarizes them by choosing
appropriate headings [10]. The formulation of
heading weight is very similar to the thematic
feature. A heading glossary is a list consisting of
all the words in headings and subheadings. Positive
weights are assigned to the heading glossary, where
the heading words will be assigned a weight relatively prime to the subheading words. The sentence
heading score of sentence is calculated by the sum
of heading weight of its constituent words.
! The cue phrase feature is proposed by Edmundson
[10] based on the hypothesis that the probable
relevance of a sentence is affected by the presence
of pragmatic words such as bsignificantQ,
bimpossibleQ, and bhardlyQ. A pre-stored cue dictionary is used to identify the cue phases, which
comprises of three sub-dictionaries: (i) bonus
words, that are positively relevant; (ii) stigma
words, that are negatively relevant; and (iii) null
words, that are irrelevant. The sentence cue score
of sentence is calculated by the sum of cue weight
of its constituent words.
Typical summarization systems select a combination of summarization features [10,25,27], the total
sentence significance score (SSS) is calculated as,
SSS ¼ a1 SSthematic þ a2 SSlocation þ a3
SSheading þ a4 SScue
ð1Þ
where SSthematic, SSlocation, SSheading and SScue are
sentence scores based on thematic feature, location
feature, heading feature and cue phrase feature,
respectively; and a 1, a 2, a 3, and a 4 are positive integers to adjust the weighting of four summarization
features. The sentences with sentence significant score
higher than a threshold are selected as part of the
summary. It has been proved that the weighting of
different summarization features does not have any
substantial effect on the average precision [25]. In our
experiment, the maximum score of each feature is
normalized to one, and the sentence significant score
is calculated as the sum of scores of all summarization
features without weighting.
50
C.C. Yang, F.L. Wang / Decision Support Systems 43 (2007) 46–61
Fig. 4. Koch curve at different abstractions level.
3.2. Fractal theory and fractal view for controlling
information displayed
Fractals are mathematical objects that have high
degree of redundancy [31]. These objects are made
of transformed copies of themselves or part of themselves (Fig. 4). Mandelbrot was the first researcher
who investigated the fractal geometry and developed
the fractal theory [31]. In his well-known example,
the length of the British coastline depends on measurement scale. The larger the scale is, the smaller
value of the length of the coastline is and the higher
the abstraction level is. The British coastline
includes bays and peninsulas. Bays include subbays and peninsulas include sub-peninsulas. Using
fractals to represent these structures, abstraction of
the British coastline can be generated with different
abstraction degrees. Fractal theory is grounded in
geometry and dimension theory. Fractals are independent of scale and appear equally detailed at any
level of magnification. Such property is known as
self-similarity. Any portion of a self-similar fractal
curve appears identical to the whole curve. If we
shrink or enlarge a fractal pattern, its appearance
remains unchanged.
Fractal view is a fractal-based method for controlling information displayed [23]. Fractal view provides
an approximation mechanism for the observer to
adjust the abstraction level and therefore control the
amount of information displayed. At a lower abstraction level, more details of the fractal object can be
viewed.
A physical tree is one of the classical examples of
fractal objects. A tree is made of a lot of sub-trees;
each of them is also a tree. By changing the scale, the
different levels of abstraction views are obtained (Fig.
5). The idea of fractal tree can be extended to any
logical tree. The degree of importance of each node is
represented by its fractal value. The fractal value of
root is 1, it propagated to other nodes with the following expression:
(
Fvroot ¼ 1
ð2Þ
Fvchild mode of x ¼ C Fv1=Dx
Nx
where Fvx is the fractal value of node x; C is a
constant between 0 and 1 to control rate of decade;
N x is the number of child nodes of node x; and D is
the fractal dimension. Mandelbrot had shown that the
fractal dimension of a tree is 1, because its total length
is finite and positive [31]. To simplify our discussion,
both C and D are considered to be 1.
A threshold value is chosen to control the amount
of information displayed, the nodes with a fractal
value less than the threshold value will be hidden
(Fig. 6). By changing the threshold value, the user
can adjust the amount of information displayed.
3.3. Fractal summarization
Advance summarization techniques take the document structure into consideration to compute the probability of a sentence to be included in the summary.
Many studies of human abstraction process had
shown that the human abstractors extract the topic
Fig. 5. Fractal view for logical tree at different abstraction level.
C.C. Yang, F.L. Wang / Decision Support Systems 43 (2007) 46–61
51
generation of the entire summary in one batch by the
traditional automatic summarization, which is ideal
for m-commerce.
Fractal summarization is developed based on the
fractal theory. In fractal summarization, the important
information is captured from the source text by exploring the hierarchical structure and salient features of the
document. A condensed version of the document that is
informatively close to the original document is produced iteratively using the contractive transformation
in the fractal theory. Similar to the fractal geometry
applying on the British coastline where the coastline
includes bays, peninsulas, sub-bays, and sub-peninsulas, large document has a hierarchical structure with
several levels, chapters, sections, subsections, paragraphs, sentences, and terms. A document is considered as prefractal that are fractal structures in their
early stage with finite recursion only [12]. A document
can be represented by a hierarchical structure as shown
in Fig. 7. A document consists of chapters. A chapter
consists of sections. A section may consist of subsections. A section or subsection consists of paragraphs. A
paragraph consists of sentences. A sentence consists of
terms. A term consists of words. A word consists of
characters. A document structure can be considered as a
fractal structure. At the lower abstraction level of a
document, more specific information can be obtained.
Although a document is not a true mathematical fractal
object since a document cannot be viewed in an infinite
abstraction level, we may consider a document as a
prefractal. The smallest unit in a document is character;
Fig. 6. An example of the propagation of fractal values.
sentences according to the document structure from
the top level to the low level until sufficient information has been extracted [11,15]. However, most traditional automatic summarization models consider the
source document as a sequence of sentences but
ignoring the structure of document. Some summarization systems may calculate sentence weight partially
based on the document structure, but they extract
sentences in a linear space. None of the current summarization model is developed on the foundation of
the hierarchical structure of documents. Fractal Summarization Model is proposed here to generate summary based on document structure. Fractal
summarization generates a brief skeleton of summary
at the first stage, and the details of the summary on
different levels of the document are generated on
demands of users. Such interactive summarization
reduces the computation load in comparing with the
Document
Chapter
Section
Subsection
Paragraph
Sentence
Character
Subsection
...
...
Term
Word
Word
Character
...
...
Paragraph
Sentence
Term
Section
...
Fig. 7. Prefractal structure of document.
...
Chapter
...
52
C.C. Yang, F.L. Wang / Decision Support Systems 43 (2007) 46–61
however, neither a character nor a word will convey
any meaningful information concerning the overall
content of a document. The lowest abstraction level
in our consideration is a term.
The Fractal Summarization Model applies techniques from fractal view and fractal image compression [1,21]. In fractal image compression, an image
is regularly segmented into sets of non-overlapping
square blocks, called range-blocks, and then each
range-block is sub-divided into subrange-blocks,
until a contractive mapping can be found to represent
this subrange-block. The Fractal Summarization
Model generates the summary by a recursive deterministic algorithm based on the iterated representation of a document. The original document is
represented as fractal tree structure according to its
hierarchical document structure. Then, the system
calculates the fractal value of each node and allocates the sentence quota base on the fractal value.
The calculation of fractal value will be discussed
later in this section.
Given a document, a user can specify compression
ratio to indicate the amount of information displayed.
The summarization system calculates the number of
sentences to be extracted as summary accordingly and
the system assigns the number of sentences to the root
of document tree as the quota of sentences. The quota
of sentences is allocated to child nodes by propagation, i.e., the quota of parent node is shared by its
child nodes directly proportional to the fractal value of
the child nodes. The quota is then iteratively allocated
to child nodes of child nodes until the quota allocated
is less than a threshold value and the range-block can
be transformed to some key sentences by traditional
summarization methods (Fig. 8). The detail of the
Fractal Summarization Model is shown as the following algorithm:
Fractal Summarization Model
1. Choose a Compression Ratio.
2. Choose a Threshold Value.
3. Calculate the Sentence Number Quota
of the summary.
4. Divide the document into range-blocks.
5. Transform the document into fractal
tree.
6. Set the current node to the root of the
fractal tree.
7. Repeat
7.1 For each child node under current node,
Calculate the fractal value of child
node.
7.2 Allocate Quota to child nodes in proportion to fractal values.
7.3 For each child nodes,
If the quota is less than threshold value
Select the sentences in the
range-block by extraction
Else
Set the current node to the child node
Repeat Step 7.1, 7.2, 7.3
8. Until all the child nodes under current
node are processed
The compression ratio of summarization is defined
as the ratio of number of sentences in the summary to
the number of sentences in the source document. It
was chosen as 25% in most literatures because it has
been proved that extraction of 20% sentences can be
as informative as the full text of the source document
[36], those summarization systems can achieve up to a
96% precision [10,22,44]. However, Teufel pointed
out the high-compression ratio abstracting is more
useful, and 49.6% of precision is reported at 4%
compression ratio [43,44]. In order to minimize the
bandwidth requirement and reduce the pressure on
computing power of handheld devices, the default
value of compression ratio is chosen as 4% for fractal
summarization on handheld devices. On the other
hand, a threshold value is the maximum number of
sentences can be extracted from a range-block that is a
node in the document tree. If the quota allocated is
larger than the threshold value, the range-block must
be divided into subrange-block. Document summarization is different from image compression, more
than one attractor can be chosen in one range-block.
The summarization by extraction of fixed number of
sentences is proven; the optimal length of summary is
3 to 5 sentences [16]. The default value of threshold is
chosen as 5 in our system.
The fractal value Fv of range-block r is computed
based on Range-block Significance Score (RBSS),
C.C. Yang, F.L. Wang / Decision Support Systems 43 (2007) 46–61
53
Document
Fractal: 1
Quota: 40
Chapter 2
Fractal: 0.5
Quota: 20
Chapter 1
Fractal: 0.3
Quota: 12
Section 1.1
Fractal: 0.1
Quota: 4
Section 1.2
Fractal: 0.15
Quota: 6
Section 1.3
Fractal: 0.05
Quota: 2
Section 2.1
Fractal: 0.1
Quota: 3
Paragraphs ...
Chapter 3
Fractal: 0.2
Quota: 8
Section 2.2
Fractal: 0.25
Quota: 10
Section 2.3
Fractal: 0.15
Quota: 7
Paragraphs...
Paragraphs...
Section 3.1
Fractal: 0.12
Quota: 5
Section 3.2
Fractal: 0.8
Quota: 3
Fig. 8. An example of fractal summarization model.
where the RBSS is computed as the sum of the
normalized weights of the sentence scores based on
the four salient features as introduced in Section 3.1.
FvðrÞ ¼
8
1
>
>
>
<
0
1 D1
C
B
C Fvð parent of rÞ @ P RBSSðrÞ
A
>
>
RBSSð xÞ
>
:
x a sibling of r
if r is root
otherwise
ð3Þ
The system utilizes the fractal value to compute the
sentence quota of each range-block by sharing the
quota of parent node directly proportional to the fractal value of the child nodes, until the sentence quota
allocated is less than the threshold value, and the
system can extract sentences directly from the
range-block. During extraction of sentences in the
range-block, the system will extract the sentences
according to the fractalized sentence weight of the
sentences.
3.4. Experimental result
It is believed that a full-length text document contains a set of subtopics [20] and a good quality
summary should cover as many subtopics as possible,
experiments showed that the fractal summarization
model produces a summary with a wider coverage
of information subtopic than traditional summarization model.
Experiment of fractal summarization and traditional summarization with four unweighted features
described in Section 3.1 was conducted on Hong
Kong Annual Report 2000. In the experiment, it is
found that the traditional summarization model
extracts most of sentences from few chapters. As
shown in Table 1, the traditional summarization
extracts 29 sentences from one chapter when the
sentence quota is 80 sentences, and 53 sentences are
extracted from top 3 chapters out of total 23 chapters,
no sentence is extracted from 8 chapters. However,
the fractal summarization extracts the sentences
evenly from each chapter. It extracts maximum 8
sentences and minimum 1 sentence from each chapter
(Table 1). The standard deviation of sentence number
extracted from chapters is 2.11 sentences in fractal
summarization against 6.55 sentences in traditional
summarization. Researchers believed that a good summary should find diverse topic areas in the text and
reduce the redundancy of information contents in the
summary [37]. Fractal summarization extracts the
sentences distributively from the document, therefore
it finds diverse topic areas and reduces the redundancy
of information at the same time.
A user evaluation is conducted. Ten subjects were
asked to evaluate the quality of summaries of 23
documents generated by fractal summarization and
traditional summarization. Both summaries of all
documents are assigned to each subject in random
order without telling the generation methods of the
summaries. The results show that all subjects consider
the summary generated by fractal summarization
method as a better summary. In order to compare
the result in more great detail, we calculate the precision as the number of relevant sentences in the summary accepted by the user divided by the number of
sentences in the summary (Table 2). The fractal summarization can achieve up to 91.25% precision and
87.16% on average, while the traditional summarization can achieve up to a maximum of 77.50% precision and 67.00% on average. One-tailed T-test has
shown that the precision of fractal summarization
model outperforms the traditional summarization significantly at 99% confidence level. Experiment of
fractal summarization and traditional summarization
with four unweighted features described in Section
3.1 was conducted on Hong Kong Annual Report
2000. In the experiment, it is found that the traditional
54
C.C. Yang, F.L. Wang / Decision Support Systems 43 (2007) 46–61
Table 1
Number of sentences extracted by two summarization models from
Hong Kong Annual Report 2000
Chapter
ID
Chapter
title
Number of
sentences
extracted in
fractal
summarization
model
1
Hong Kong:
Asia’s World
City
Constitution and
Administration
The Legal System
The Economy
Financial and
Monetary affairs
Commerce and
Industry
Employment
Primary Production
Education
Health
Social Welfare
Housing
Land, Public
Works and Utilities
Transport
Infrastructure
The Environment
Travel and Tourism
Public Order
Communications,
the Media and
Information
Technology
Religion and
Custom
Recreation, Sport
and the Arts
Population and
Immigration
History
6
3
4
1
2
5
8
0
14
29
6
10
2
1
2
1
1
1
4
2
0
1
0
0
0
0
5
1
4
1
5
6
3
0
1
1
2
6
2
0
5
3
3
1
Table 2
Precision of summaries of Hong Kong Annual Report 2000 for two
summarization models
5
3
User ID
Fractal
summarization
model (%)
Traditional
summarization
model (%)
User
User
User
User
User
User
User
User
User
User
81.25
85.00
80.00
85.00
88.75
81.25
91.25
86.25
85.00
87.50
71.25
67.50
56.25
63.75
77.50
61.25
76.25
58.75
65.00
72.50
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
Number of
sentences
extracted in
traditional
summarization
model
tence from each chapter (Table 1). The standard deviation of sentence number extracted from chapters is
2.11 sentences in fractal summarization against 6.55
sentences in traditional summarization. Researchers
believed that a good summary should find diverse
topic areas in the text and reduce the redundancy of
information contents in the summary [37]. Fractal
summarization extracts the sentences evenly from
the document; therefore it finds diverse topic areas
and reduces the redundancy of information at the
same time.
We have also conducted another experiment using
the tested TIPSTER Text Summarization Evaluation
(SUMMAC) data. The TIPSTER Text Summarization
Evaluation (SUMMAC) is the first large-scale, developer-independent evaluation of automatic text summarization systems [33]. The documents for the
TIPSTER evaluation are drawn from Text Retrieval
(TREC) [19] CDs 4 and 5. The categorization task in
SUMMAC focuses on generic summaries. The task
sought to find out whether a generic summary could
effectively present sufficient information to allow an
analyst to quickly and correctly categorize a document. In order to compare the performance with other
summarization systems, we have conducted the categorization task to evaluate the performance of the
fractal summarization model. We have followed the
standard TIPSTER setting to conduct the classification task using fractal summarization. 100 documents
are selected from the TIPSTER corpus, and 10% of
sentences are extracted from each document by fractal
summarization system. 15 subjects are involved in the
summarization model extracts most of sentences from
few chapters. As shown in Table 1, the traditional
summarization extracts 29 sentences from one chapter
when the sentence quota is 80 sentences, and 53
sentences are extracted from top 3 chapters out of
total 23 chapters, no sentence is extracted from 8
chapters. However, the fractal summarization extracts
the sentences evenly from each chapter. It extracts a
maximum of 8 sentences and a minimum of 1 sen-
1
2
3
4
5
6
7
8
9
10
C.C. Yang, F.L. Wang / Decision Support Systems 43 (2007) 46–61
experiment. The subjects need to classify the documents based on the sentences extracted for each document. The experiment result shows that the fractal
summarization system has a similar precision as
other summarization systems, and it has a better
recall. The fractal summarization achieves an Fscore [40] of 0.63 but other summarization systems
achieve a mean F-score of 0.42. Therefore, the fractal
summarization system outperforms other summarization systems. As the documents used are relatively
shorter in their length, most of them contain less than
100 sentences.
3.5. Visualization of fractal summarization on handheld devices
The summary generated by Fractal Summarization
Model is represented in a hierarchical tree structure.
The hierarchical structure of summary is suitable for
visualization of information on handheld devices.
However, a summary displayed in a small area of
handheld devices without visualization effect is still
difficult to read, it can be further enhanced by displaying the sentences in different font sizes according to their importance to help user to focus on
important information and search for information
easily.
WML is the markup language supported by wireless handheld devices. The basic unit of a WML file is
55
a deck; each deck must contain one or more cards.
The card element defines the content displayed to
users, and the card cannot be nested. Each card
links to another card within or across decks. Nodes
on the fractal tree of fractal summarization model are
converted into cards, and anchor links are utilized to
implement the tree structure. Given a card of a summary node, there may be a lot of sentences or child
nodes. A large number of sentences in a small display
area make it difficult to read. Fisheye View is a
visualization technique to enlarge the focus of interest
and diminish the information that is less important
[13]. When a user look at an object, the objects nearby
are shown in a larger visual size; and the visual size of
other objects is decreased inversely proportional to
their distances to the focus point.
In our system, we have modified the fisheye view.
The size of an object does not depend on its distance
from the focus point, but depends on the significance
of the object. The sentences are displayed in different
font sizes according to their importance. We have
implemented the fisheye view with 3-scale font
mode available for WML. The sentences or child
nodes are sorted by their sentence weights or fractal
value and separated evenly into three groups. The
group with highest value is displayed in bLargeQ
font size, and the group with middle value and the
group with lowest value are displayed in bNormalQ
and bSmallQ font size, respectively.
Fig. 9. Screen capture of WAP summarization system. (a) Hong Kong Annual Report 2000; (b) Chapter 19 of Hong Kong Annual Report 2000,
dCommunication, the Media and Information TechnologyT.
56
C.C. Yang, F.L. Wang / Decision Support Systems 43 (2007) 46–61
The prototype system using Nokia Handset Simulator is presented in Fig. 9. The document presenting
is the Hong Kong Annual Report 2000. There are
totally 23 chapters in the annual report, 8 of them
are in large font, which means that they are more
important, and the rest are in normal font or small
font according to their importance to the report (Fig.
9a). The number inside the parentheses indicates the
number of sentences under the node that are extracted
as part of the summary. The main screen of the Hong
Kong Annual Report 2000 gives user a general idea of
overall information contents and the importance of
each chapter. If the user wants to explore a particular
node, user can click the anchor link, and the handheld
device sends the request to the WAP gateway, and the
gateway then decides whether to deliver another menu
or the summary of the node to the user depends on its
fractal value and quota allocated. Fig. 9b shows the
summary of Chapter 19 of Hong Kong Annual Report
2002, bCommunication, the Media and Information
TechnologyQ.
A handheld PDA is usually equipped with more
memory, and the complete summary can be downloaded as a single WML file to the PDA through local
synchronization. To read the summary, the PDA is
required to install a standard WML file reader, i.e.,
KWML for Palm [24].
4. Summarization for financial news delivery on
handheld devices
Fractal summarization model summarizes the documents based on hierarchical document structure. In
addition to large text document, a lot of other information sources also exhibit hierarchical document structure, such as web-site and newspaper. Due to the huge
information available, it is difficult to browse these
information sources on handheld devices. Automatic
summarization is a possible solution. Theoretically, the
fractal summarization is capable to summarize to all
these information sources as long as the calculation of
fractal value is well formulated. As financial news is
critical in decision making, we shall modify the fractal
value formula of generic fractal summarization in order
to summaries the financial news and we shall demonstrate financial news delivery with fractal summarization on handheld devices.
4.1. Fractal summarization of financial news
Fractal summarization model performs the summarization based on hierarchical document structure. In addition to large text documents, a lot of
other documents also exhibit hierarchical tree document structure, such as web-site, newspaper, etc.
The fractal summarization model is capable to
summarize these documents based on their structure
and their relationships in categorization; therefore, it
is a powerful tool in providing m-services of real
time information delivery. As present, a lot of
electronic news delivery services have been provided. An example of the fractal summarization
model being used to summarize the financial
news available from Internet is presented in this
section.
Newspaper is one of the documents that exhibit
the well-defined hierarchical document structure. At
present, there is a lot of electronic news delivery
services provided for PC, and most of them provide
summarization tools to help user to search information, such as Lycos Financial Feed System with
summarization system from Diyatech [9], YellowBrix with Inxight’s Summarizer [52] and Columbia’s
Newsblaster [34]. However, summarizers for PC
platform are not adaptable to mobile devices directly.
Moreover, the existing commercial summarizers are
indeed extracting the first few sentences from the
document or using the primitive summarization
model without considering the hierarchical structure
of documents or the organization of information.
Yahoo!News [47] is one of the most popular online
content providers. There are 21 categories in the
Yahoo!News. Moreover, each of the categories will
be sub-divided into subcategories. Take the Business
category as an example (Fig. 10), this category contains financial news and it is sub-divided into six
sub-categories, namely, Economy, Stock Markets,
Earnings, Personal Finance, Industries and Commentary. Each sub-category contains around 10 news
articles. Each news article is a tree structure by itself.
For some longer news article, there may exist more
than one section, and each section contains few
paragraphs, and paragraph contains sentences.
The Fractal Summarization of Yahoo!News is very
similar to the fractal summarization of large text
document, only some minor modifications are
C.C. Yang, F.L. Wang / Decision Support Systems 43 (2007) 46–61
57
Yahoo! News - Business
Weight: 1
Quota: 40
Commentary
Weight: 0.25
Quota: 10
Free Flight
Weight: 0.0075
Quota: 3
News ...
Earning
Weight: 0.25
Quota: 10
Economy
Weight: 0.2
Quota: 8
News ...
News ...
Industries
Weights: 0.1
Quota: 4
Personal Finance
Weight: 0.15
Quota: 6
Stock Market
Weight: 0.05
Quota: 2
News ...
Fig. 10. Fractal summarization of yahoo! news-dbusinessT category.
required to demonstrate the characteristic of the
Yahoo!News.
! Firstly, the headings of categories and subcategories do not have a direct impact on the content
of news under the branch; it serves for classification purpose only. As a result, the heading method
will consider the headings of news articles only. In
addition, the headings of categories can be used for
personalization of news delivery, the user can set
his preference of each category in advance and the
system will adjust the weights accordingly. Alternatively, the preference can be constructed by autolearning of machine in the middle-tier. The WAP
gateway can analyze the reading behavior of user
and predict the user’s preference.
! The location feature in traditional summarization
assumes that the text unit in the beginning or ending
is more important. The news articles inside a subcategory are sorted in chronological order. The most
recent news is usually considered as more important. Therefore, we propose calculating the location
weight of a news article by its chronological position
in the subcategory or the time-lag between the news
event and browsing time. However, when the system traces the summarization tree down to a node
inside a news article, the generic location method in
fractal summarization will be adopted.
! In order to provide a glimpse of every article, each
news article will receive a sentence quota with at
least one sentence.
4.2. Financial news delivery to handheld devices
In order to minimize the bandwidth requirement
and reduce the pressure on computing power of handheld devices, the summarization of Yahoo!News will
be conducted in two levels. As high-compression ratio
abstracting is more useful [43] and it can save the
network bandwidth, the fractal summarization system
generates a brief skeleton of summary with compression ratio equals to 4% at the first stage. The details of
the summary at different levels of the news tree are
generated on demands of users.
When handheld device retrieves the financial news
from Yahoo! News-Business, the system will first show
a card containing with 6 subcategories of dBusinessT
category (Fig. 11). In the figure, three subcategories of
Business category are displayed in large font, which
means that they are more important; and the rest are in
normal font or small font according to their importance.
The skeleton of news gives user a general idea how the
news articles are organized, and the user can decide
which subcategory to go into details. When the user
clicks the anchor link of subcategory, the WAP gateway
will deliver a card depends on the quota allocated. If a
large quota is allocated to the subcategory, the system
will show another card containing of index of news
article. However, if the quota is less than a threshold
value of 5 sentences, the system will show a card with
the summary of all news articles in the subcategory. In
the summary page, when the user clicks the anchor link
dMoreT at end of sentences, the system will generate the
summary for the corresponding news articles with
compression ratio 20%, because it has been proved
that extraction of 20% sentences can be as informative
as the full text of the source document [36]. On the
other hand, the user can click the anchor link dFullT to
view the full text of the news articles. Such interactive
summarization reduces the computation load in comparing with the generation of the entire summary in one
batch by the traditional automatic summarization,
which is ideal for m-services.
4.3. Future work
In current stage, the fractal summarization is capable
to process textual information only. However, there is a
58
C.C. Yang, F.L. Wang / Decision Support Systems 43 (2007) 46–61
Fig. 11. Financial news delivery system on mobile devices.
lot of information available in multimedia format on the
Web. Information delivery of multimedia document
will be one of the key research topics in the near future.
As the multimedia documents require a much higher
bandwidth than textual documents, this problem cannot
be resolved solely by the current steaming technology.
Summarization of multimedia documents is required
for information delivery to mobile devices. The
research work of spoken document was initiated in
the spoken language track of TREC 1997 [14]. The
research of spoken document summarization starts in
2000 [53,54]. Summarization of video has also been
investigated [45,46]. It would be a great challenge to
move the proposed model to multimedia documents.
The summarization of multimedia document is a complementary to the proposed model. Nowadays, most of
the mobile devices are speech-based. With the summarization of spoken documents, the information can
be easily delivered to speech-based mobile devices.
This will certainly increase the popularity of the proposed model. Moreover, it can provide information
access for the blind as well [17].
5. Conclusion
Mobile commerce is a promising addition to the
electronic commerce by the adoption of portable hand-
C.C. Yang, F.L. Wang / Decision Support Systems 43 (2007) 46–61
held devices. However, the mobile computing should
not be limited to user-centered m-services applications
only, it should be extended to decision making in an
organization. With a fast paced economy, organization
need to make a decision as fast as possible, access to
large text documents or other information sources is
important during decision making. Unfortunately, there
are many shortcomings of the handheld devices, such
as limited resolution and narrow bandwidth. In order to
overcome the shortcomings, fractal summarization and
information visualization are proposed in this paper,
which are critical in decision support in an m-organization. The fractal summarization creates a summary in
hierarchical tree structure and presents the summary to
the handheld devices through cards in WML. The
adoption of keyword feature, location feature, heading
feature, and cue feature are discussed. Users may
browse the selected summary by clicking the anchor
links from the highest abstraction level to the lowest
abstraction level. Fractal views are utilized to filter the
less important nodes in the document structure, sentences are displayed in different font size to enlarge the
focus of interest and diminish the less significant sentences. Such visualization effect draws users’ attention
on the important content. The three-tier architecture is
presented to reduce the computing load of the handheld
devices. The proposed system creates an information
visualization environment to avoid the existing shortcomings of handheld devices for mobile commerce.
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61
Christopher C. Yang is an associate professor in the Department of
Systems Engineering and Engineering Management at the Chinese
University of Hong Kong. He received his BS, MS, and PhD in
Electrical and Computer Engineering from the University of Arizona. Before he joined the Chinese University of Hong Kong, he
was an assistant professor in the Department of Computer Science
and Information Systems and the associate director of the Authorized Academic JavaSM CampusSM at the University of Hong Kong.
He has also been a research scientist in the Artificial Intelligence
Laboratory in the Department of Management Information Systems
at the University of Arizona. His recent research interests include
cross-lingual information retrieval, multimedia information retrieval, digital library, information visualization, Internet searching,
automatic summarization, information behavior, and electronic
commerce. He has published over 100 refereed journal and conference papers in Journal of the American Society for Information
Science and Technology, IEEE Transactions on Image Processing,
IEEE Transactions on Robotics and Automation, IEEE Computer,
Information Processing and Management, Decision Support Systems, Graphical Models and Image Processing, Optical Engineering,
Pattern Recognition, International Journal of Electronic Commerce,
Applied Artificial Intelligence, SIGIR, ICIS, WWW, and more. He
was the chairman of the Association for Computing Machinery
Hong Kong Chapter, the program co-chair of the First International
Conference on Asia Digital Library, and a program committee and
organizing committee member for several international conferences
in digital library, information systems, electronic commerce, computer vision, image processing, and information retrieval. He has
frequently served as an invited panelist in the NSF Digital Library
Initiative Review Panel and the NSF Information Technology
Research Review Panel in US.
Fu Lee Wang is an Instructor in the Department of Computer
Science, City University of Hong Kong. He received B.Eng. (Hon)
degree in Computer Engineering and M.Phil. degree in Computer
Science and Information Systems from the University of Hong Kong,
and PhD degree in Systems Engineering and Engineering Management from Chinese University of Hong Kong. His current research
interests focus on document summarization, digital library, and
information retrieval. His research work has been appeared in such
journals as Journal of the American Society for Information Science
and Technology, and Information Processing Letters.