ELABORATION IN OBJECT DESCRIFFIONS THROUGH EXAMPLES 
Vibhu O. Mittal 
Department of Computer Science 
University of Southern California 
Los Angeles, CA 90089 
USC/Information Sciences Institute 
4676 Admiralty Way 
Marina del Rey, CA 90292 
Abstract 
Examples are often used along with textual descrip- 
tions to help convey particular ideas - especially in 
instructional or explanatory contexts. These accompa- 
nying examples reflect information in the surrounding 
text, and in turn, also influence the text. Sometimes, 
examples replace possible (textual) elaborations in the 
description. It is thus clear that if object descriptions 
are to be generated, the system must incorporate strate- 
gies to handle examples. In this work, we shall inves- 
tigate some of these issues in the generation of object 
descriptions. 
INTRODUCTION 
There is little doubt that people find examples very ben- 
eficial in descriptions of new or complex objects, rela- 
tions,orprocesses. Various studies have shown that the 
inclusion of examples in instructional material signifi- 
cantly increases user comprehension (for e.g., (Houtz, 
Moore & Davis, 1973; MacLachlan, 1986; Pirolfi, 
1991; Reder, Charney & Morgan, 1986; Tennyson 
& Park, 1980)). Users like examples because exam- 
pies tend to put abstract, theoretical information into 
concrete terms they can understand. Few generation 
systems have attempted to make significant use of ex- 
amples, however. In particular, most systems have 
not integrated examples in the textual descriptions, but 
have used them mostly on their own, independently of 
the explanation that may also have been provided at 
that point. However, examples cannot be generated in 
isolation, but must form an integral part of the descrip- 
tion, supporting the text they help to illustrate. 
Most previous work (especially in the context of 
tutoring systems) focused on the issue offinding useful 
examples (for e.g., Rissland's CEG system (1981) and 
Ashley's HYPO system (Ashley, 1991; Rissland & 
Ashley, 1986; Rissland, 1983)). Work by Woolf and 
her colleagues considered issues in the generation of tu- 
torial discourse, including the use of examples (Woolf 
& McDonald, 1984; Woolf & Murray, 1987), but their 
315 
analysis did not address specific issues of integrated 
example and language generation. 
In this paper, we build upon some of these stud- 
ies and describe the issues in generating descriptions 
which include examples in a coordinated, coherent 
fashion, such that they complement and support each 
other. 
AN EXAMPLE 
Consider for inst,'mce, the example in Figure 1, from 
a well known introductory book on the programming 
language LISP. It describes an object (a data structure) 
called a "fist." There are a number of issues that can 
be immediately seen to be relevant: 
I. Should the system choose to elaborate on the object 
attributes in text, or through the use of examples? 
For instance, the information in Figure I could also 
have been expressed textually as: "A list always be- 
gins with a left parenthesis. Then come zero or more 
pieces of data (called the elements of a list), and a 
right parenthesis. Data elements can be of any LISP 
type, including numbers, symbols and strings". In 
the figure, the examples arc used to elaborate on two 
aslaeCtS of the data-elements: the variable number 
of the elements, and the different types of which 
these elements may belong to. In some contexts, the 
examples tend to re-iterate certain aspects (in this 
case, the number was mentioned in the explanation 
as well), while in others, the examples tend to elab- 
orate on aspects that are not mentioned explicitly in 
the description (in our case, the type information). 
2. Should the system use one example, or multiple ex- 
amples? Consider for instance, the following exam- 
ple of a LISP list: 
(FORMAT T "~2% ~ A ~ A - A" 12345678 
' James ' Smith (address person) ) 
It is not entirely obvious that single examples of the 
type above arc always the most appropriate ones, 
A list always begins with a left parenthesis. Then come 
zero or more pieces of data (called the elements of a 
list) and a right parenthesis. Some examples of lists 
are: 
(AARDVARK) ;;; an atom 
(RED YELLOW GREEN BLUE);;; many atoms 
(2 3 5 11 19) ;;; numbers 
(3 FRENCH FRIES) ;;; atoms & numbers 
A list may contain other lists as elements. Given the 
three lists: 
(BLUE SKY) (GREEN GRASS) (BROWN EARTH) 
we can make a list by combining them all with a 
parentheses. 
((BLUE SKY) (GREEN GRASS) (BROWN EARTH)) 
Figure 1: A description of the object LIST using ex- 
amples (From (Touretzky, 1984), p.35) 
even though such examples are frequently seen in 
technical reference material. The system must there- 
fore be able to make reasonable decisions regarding 
the granularity of information to be included in each 
example and structure its presentation accordingly. 
3. If there are multiple examples that are to be pre- 
sented, their order of presentation is important too. 
Studies has shown that users tend to take into ac- 
count the sequence of the examples as a source of 
implicit information about the examples (Carnine, 
1980; Litchfield, IMiscoll & Dempsey, 1990; Ten- 
nyson, Steve & Boutwell, 1975). For instance, in 
Figure 1, the first and second examples taken to- 
gether illustrate the point that the number of data 
elements is not important. 
4. When are 'prompts' necessary? Examples often 
have attention focusing devices such as arrows, 
marks, or as in the Figure 1, extra text, associated 
with them. These help the user disambiguate the 
salient from the irrelevant. What information should 
be included in the prompts, and in the case of text, 
how should be be phrased? 
5. How should the example be positioned with respect 
to the text? Studies of instructional texts reveal that 
examples can occur before the text (and the text 
elaborates upon the example), within the text, or (as 
in our figure), after the text (Feldman, 1972). 
There are other issues that need to be considered 
in an integrated framework - some of these that affect 
most of the issues raised above are the audience-type, 
the knowledge-type (whether the concept being de- 
scribed is a noun or a relation for instance) and the 
text-type (tutorial vs. reference vs. report, ete). The 
316 
DESCRI~I~LIST 
I 
DATA 
l.mT $1t'NTA.CT\]C ~ 
I natm.~z 
.I .I .I 111'~1111~. I~II~IIIM _. INI~QM._ 
Figure 2: Plan skeleton for listing the main features of 
a LIST. 
4. 
NU1BIR TYPB 
M M 
M M- 
,~M m/z 
Pit~M ID.CX~OUI~ LIST 
OF Lm~ 
I I 
M lqtolKr \[ - ATOMS - NU~K~ --/aDM$+ -- ~/~-/d$'~ 
FiN 3: Partial text plan for generating the LIST 
examples. 
issue of how the examples are selected (generated vs. 
retrieved is also an important issue, but we shall not 
discuss that here. 
STATUS OF WORK 
We are investigating these issues by implementing a 
system that can generate examples within explanatory 
contexts (within theEES framework (Neches, Swartout 
& Moore, 1985; Swartout & Smoliar, 1987)) using the 
Moore-Paris planner (1992, 1991 ) for discourse gener- 
ation. Our initial system is for the automatic generation 
of documentation for small sub-sets of programming 
languages. One reason for this choice is that it al- 
lows us to study a variety of example-rich texts in a 
relatively unambiguous domain. A partial text-plan 
generated by our planner for the description given in 
Figure 1 is given in Figures 2 and 3. It shows some of 
the communicative goals that the planner needs to be 
able to satisfy in order to generate some of the simple 
object descriptions in our application. These descrip- 
tions can make use of examples (instead of tex0 to 
list and describe feature elaborations, or use them in 
conjunction with a textual description to clarify and 
illustrate various points. 
Among issues that we plan to study are the differ- 
ences between opportunistic generation of examples 
and top-down planning of text with examples, and the 
effects arising from differences in the knowledge type, 
the text-type and other sources of information. 
Acknowledgments 
Thanks to C6cile Paris for critical discussions, different 
perspectives and bright ideas. This work was supported 
in part by the NASA-Ames grant NCC 2-520 and under 
DARPA contract DABT63-91 42-0025. 

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