• A PRINCIPLED REPRESENTATION OF ATTRIBUTIVE DESCRIPTIONS 
FOR GENERATING INTEGRATED TEXT AND INFORMATION 
GRAPHICS PRESENTATIONS 
Nancy •Green*, Giuseppe Carenini**, and Johanna Moore.** " 
*Carnegie Mellon University, **University of Pittsburgh 
nancy.green@cs.cmu.edu, {jmoore, carenini} @Cs.pitt. edu 
Abstract 
This paper describes a media-independent, compositional, )lan-based approach to represent- 
ing attributive descriptions for use in integrated text and graphics generation. An attributive 
description's main function is to convey information directly contributing to the communicative 
goals of a discourse, Whereas a referential description's only function is to enable the audience 
to identify a particular referent. This approach has been implemented as part of an architecture 
for generating integrated text and information graphics. Uses of referential and attributive de- 
scriptions are represented as two distinct types of communicative acts in a media-independent 
plan. It is particularly important to distinguish the two types of acts, since theyhave different 
consequences for dialogue and text generation, and for graphic design, 
•1 Introduction 
This paper describes a media-independent, compositional, plan-based approach to representing 
attributiv e descriptions for use in integrated text and graphics generat!on. An attributive descrip- 
tion's main function is to convey information directly contributing to the communicative goals of 
a discourse, whereas a referential description's only function is to enable the audience to identify a 
particular referent \[Donnellan1977, Kronfeld1986\]. While the generation of referential descriptions 
has received considerable attention in text and multimedia generation, the generation of attributive 
descriptions has received • relatively little attention in computational linguistics. 
: However, such descriptions are pervasive in the type of presentations which is the focus of 
our research. We are developing systems that automatically generate presentations consisting • 
of coordinated text and information graphics (graphics for presenting abstract, quantitative or 
relational information as opposed to depictions of real-world objects or processes). For example in 
our current implementation, the •system produces analyses and summarizations of large amounts of 
data created by a transportation scheduling program. In this domain, it is necessary to generate 
descriptions of aggregate quantities of complex attributes such as total port capacity of all ports and 
90~ of the total weight• of the cargo arriving by day 25. Furthermore, in this genre both referential 
and attributive uses of descriptions occur. .. 
in our approach, presentations are "generated •using .an architecture that integrates hierarchi- 
cal Planning to achieve media-independen t communicative goals with task-based graphic design. 
This architecture has been implemented in a prototype system. The focus of this paper is on 
the representation and role of attributive descriptions in the architecture. First, we describe the 
referential-attributive distinction and its importance in dialogue and text generation. Next, we dis- 
cuss its importance in task-based graphic design. After providing an overview of our architecture, 
we describe how attributive descriptions are planned. We conclude with a survey of related work. 
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2 Referential-Attributive Distinction in Language 
\[Donnellan1977\] describes two different possible uses of definite descriptions: 1 An attributive de- 
scription's main function is to convey information directly contributing to the communicative goals 
of a discourse, whereas a referential description's only function is to enable the audience to identify 
a particular referent. This is a useful distinction for dialogue systems. In the case of failure of a 
referential description, a system might try to identify the referent again by giving:an alternate de- 
scription, as illustrated in (1) below. However, when a description is used attributively, the content 
of the description plays a different role. In (2a), the required textbook for CS500 is used attributively 
to indirectly inform the user of how she might assess the difficulty of CS500 herself; the content 
of the description contributes to the user's recognition of the system's reason for suggesting that 
she read the book. In contrast in (2b), where an alternate description is used, the user is unable 
to recognize the systems's intention. In contrast to (1), (2c) illustrates that when all attributive 
description fails, a different type of followup by the system is required, one that explicates its in- 
tention. Also, as Donnellan points out and as can be seen by comparing (!) and (2c), the same 
description (the required textbook for CS500) can be used either referentially or attributively on 
different occasions depending on the speaker's intentions. 
(!) User: I want to read an introductory AI book. What do you recommend? 
System: The required textbook for CS500. 
User: Which book is that? 
System: Artificial Intelligence, by Richardson. 
User: Oh:. 
(2a) User: How difficult is CS5007 
System: Read the required textbook for CS500. 
(2b) User: • How difficult is CS5007 
Syste,n: Read Artificial Intelligence, by Richardson. 
User: Why? 
System: It is therequired textbook for CS500. 
(2c) User: How difficult is CS500? 
System: Read the required textbook for it. 
User: VV'hy? 
System: If you find the textbook difficult, then you'll find the course difficult. 
In addition to its importance in determining appropriate dialogue fo!lowup behavior, the referential- 
attributive •distinction is important for generating effective text. As was •shown in (2a), the content 
of an attributive description may contribute directly to achieving communicative goals. To give 
another example, suppose that a user, who wants to buy a house in Somerset County, has asked for 
information about realtors serving Somerset County. 2 The overall goal of the system is for the User 
to believe that it may be beneficial to do business with a certain real estate agency, Realtors Inc. 
In that case, the system might generate (3), where (3)ii is intended to provide motivation for (3)i. 
That is, the description the city with the largest population in Somerset County was selected bythe 
system for its motivational value. In a system that does not distinguish referential from attributive 
(i.e., treats all uses of descriptions as referential), there is nothing preventing it from generating 
(4) or (5) instead, assuming that the city with the largest population in Somerset County, Berlin,. 
and the city with the worst pollution in Somerset County are three descriptions of the same object 
(which we refer to below by the internal system identifier $BERLIN). 
i Although DonneUan did not address uses of indefirdte descriptions, following \[Kronfeld1990\] we apply Dorme!Ian's 
distinction to them as Well. Also, to be precise, we are interested in what \[Kronfeld1986, Kronfeld1990\] terms :the 
modal aspect of Donnellan's distinction. 
:The information in this'and all other examples in the paper is fictitious. 
19 
(3)i. We recommend Realtors Inc. 
ii. Realtors Inc. serves the city with the largest population in Somerset County. 
(4)i. We recommend Realtors Inc. 
ii. Realtors Inc. serves Berlin. 
(5)i. We recommend Realtors Inc. 
ii. Realtors Inc. serves the City with the worst pollution in Somerset County. 
• \ 
However, (4)ii is not as effective as (3)ii if the user doesn't know or have in mind that Berlin 
has the largest population. Even worse, (5)ii might have an effect opposite to the one intended. 
A possible solution might be for the system to include as an additional proposition to be asserted 
with (4), the proposition that tBERLIN is the city with the largest population in Somerset County, 
yielding (6). On the other hand, there is nothing in the supposed underlyling representation of (6) 
to prevent (7) from beinggenerated, which may have a less than desirable effect, 
• (6)i, We recoinmend Realtors inc .... 
ii. Realtors Inc. serves Berlin. : 
iii. Berlin has the largest population in Somerset County. 
(7)i. We recommend Realtors Inc. 
ii. •Realtors Inc. serves the city with the worst pollution in Somerset.County. 
iii. That. city has the largest population in Somerset County. 
3 The Role of Attributive Descriptions in Task-Based Graphic 
Design ' 
As this section will illustrate shortly, different graphic designs may enhance or detract from a 
user's performance of certain types of perceptual and cognitive tasks. The philosophy of task-based 
graphic design is to design an information graphic based upon which perceptual and cognitive tasks 
the user wants or needs to perform. In our architecture (described more fully in the next section)~ 
the graphics generator reasons about what user tasks would enable the system's presentation'goals 
to be achieved, so that graphics can be designed to support those tasks (and thus support the 
presentation goals). Note that since the descriptions in Our domain of application are often fairly 
• complex (e.g., 90~o of the total weight of the cargo arriving by day 25), we assume that a composi- 
ti0nalappr0ach to representing attributive descriptions will facilitate the automatic transformation 
of presentation goals to user tasks. 
To see how different graphic designs about the same data may facilitate different tasks, consider 
Figure 1. In (a), the table shows that Arlington's population is .5K, Berlin's is 1K, etc. Moreover, 
• it is possible to compute from the data shown in it that Arlington's population is half that of 
Berlin's, that Berlin has the largest population, and that Berlin's population is greater than the 
population of all of the other towns combined. To facilitate just task (A), the task of looking up 
the population of a town given its name, then this table would be adequate. On the other hand, 
a bar chart such as the one Shown in (b) would better support both tasks (h) and (B), where 
(B) is the task 0fdetermining the largest and the smallest town. (Each vertical bar represents a 
particular town and ~the height of a bar represents the population of the town represented by the 
bar.) Ordering the towns by population size, as in (c), further facilitates task (B), as can be seen 
by comparing (b) to (c). However, task (C), the task of comparing Berlin's population to the total 
population of all of the other towns, would be facilitated by the chart shown in (d). In it, task 
(C) is facilitated by enabling the user to count the divisions of each bar. Also, if task (A) is not 
required, it is not necessary t O provide numeric values on the horizontal axis in (d). 
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Population of four cities 
.,K :2, .,K 
, .2K j 
(a) ~ (b) (c) ~ 
~ Real¢ors Inc. I Berlin ~OOOOOOOOO 
Value Inc. I Arlington W ~OOOO Villas Inc. I 
\]ES =a re Inc. i Durham jO0 
Villas Inc. Ca~ iO i O = .IK 
Esc.av.e "rnc. 
(e) 
......... j_ 
Town I Pop. 
Berlin '~OOOO OOOOO t 
Arl ingnon ~OOOO 
Durha~a IOO \ 
CaW : O ~ O =.IK I 
(d) 
I TO~ 
Arlington 
I Pop ° i Agenc~ : : 
I i Value Inc. 
I .SK ! Villas ~nc. 
Berlin 
Cary 
"I Durham 
I IK ! Realuors Inc. 
.IK Villas ~nc. 
Esnate Inc. 
tl " .2K i Estane In c . • _ ........ 
(0 
...................... : .... : ........................ :. .... 
Figure h Graphics Supporting Different Tasks 
Si~lce in our approach the graphics generator reasons about what user tasks would enable the 
system's presentation goals to be achieved, it is important for the system to distinguisli cases where 
the content of a description itself directly contributes to the presentation's goals, i.e., where the 
content has an attributive rather than a referential function. For instance, suppose that a system 
must design a graphic supporting •the presentation goals described for example (3) above. These 
goals could• be achieved by the user's successful performance of task (B)above, and additionally, 
task (D), the task of looking up the real estate agency for that town. These tasks would be 
facilitated by a graphic such as (e)in Figure i, which facilitates both tasks. In contrast, if the 
system provided only table (f) of Figure 1, task (D) but not task (B) would be facilitated, and.thus 
the overall presentation might not be as effective. 
4 Overview of Generation Architecture 
As reported in a previous paper \[Kerpedjiev et a1.1997\], we are investigating the integration of 
two complementary approaches to automatic generation of presentations: hierarchical planning to 
achieve communicative goals and task-based graphic design. Many researchers in natural lan- 
guage processing, e.g., \[Moore1995\], have modeled presentation design as a process of hierar- 
chical planning to achieve communicative goal s. Researchers in graphics have emphasized the 
need to design presentations that support the perceptual and logical tasks a user must perform 
\[Beshers and Feiner1993, Casneri991, Roth and Mattis1990\]. In our hybrid approach, a hierarchb 
cal planner \[Young1994\] is used to refine genre-specific but media-independent presentation• goals 
into genre-independent and media-independent subgoals. (For Simplicity, in the rest of this pa- 
per we shall refer to the genre-independent and media-independent level of the plan just as the 
media-independent level.) These media-independent goals are achieved by media-independent il- 
Iocutionary actions \[Searle1970\], e.g., Assert, and Recommend, which themselves are decomposed 
into media-independent actions that correspond to attributive and referential uses of descriptions. 
(Tile language used in our current system to express the content.of illocutionary acts and goals 
is described in \[Green et a1.1998\]. In addition to application-specific terms, the language includes 
21 
more broadly applicable terms for expressing quantitative relations and aggregate properties.) 
The media-independent plan is used by two media-specific generators (one for text, another 
for graphics) to create parts of the presentation. (The problems of media-allocation, how the sys- 
tem decides what parts of the presentation to realize in which media, and media-coordination, 
how it coordinates information conveyed in both media, are beyond the scope of this paper.) 
The text generator converts parts of the plan (as determined by the media-allocatiom compo- 
nent) to funct!onal descriptions (FDs) of sentential units, which specify, for example, semantic 
predicate-argument structure, open-class lexical items, and aspects of sentence structure with 
pragmatic import. The FDs are subsequently realized by a general-purpose sentence genera.tor 
(FUF/SURGE) \[Elhadad and Robin1996\]. (Decisions regarding the content of referential descrip- 
tions and anaphora, which are made by the text generator, are beyond the scope of this pa- 
per.) The first stage of thegraphics generator converts parts of the plan (as determined by the 
media-allocation component)to a sequence of logical user tasks that will enable the presenta- 
tion's goals to be achieved; the task sequence is then inPut to the SAGE graphic design system 
\[Roth and Mattis1990, Chuah et a1.1995, Roth et a1.1994\], which automatically creates a graphic 
supporting the user's tasks. 3 For example, the presentation goal that the user know the popula- 
tion of Arlington would be enabled if the user were able to perform the sequence of logical tasks 
of searching for Arlington in a graphic, finding its population attribute, and then looking up the 
value; furthermore, these tasks could be performed using a graphic such as (a) in Figure 1. (The 
process of converting acts of the plan to tasks is partly described in \[Kerpedjiev et a1.1998\] and is 
• beyond the scope of this paper.) 
5 Planning Attributive Descriptions 
This section describes • how the two types of actions corresponding to attributive and referential uses 
of descriptions are created and represented in the media-independent planning phase of generation 
in our system. Our system uses media-independent presentation operators to perform content• se- 
lection and high~level •organization of the presentation. For example, Figure 2. shows a simplified 
version of the presentation operator that would be used to generate (3) above, in the formalism used 
by the presentation planner \[Young1994\]. The strategy encoded in this decomposition is to recom- 
• mend anaction, as in (3)i, and to provide information that may motivate the audience• to adopt 
the recommendation, as in (3)ii. 4 The plan parameter ?p2 would be instantiated-with the propo- 
sition describing the recommended action. 5 The Motivate plan constraint of the operator would 
instantiate the plan variable ?pl with the proposition expressed in (3)ii. In our current system, 
the Search for a proposition satisfying a constraint such as the Motivate constraint in the example 
is •performed by accessing a database created by a domain-specific data analysis component. For 
• example, in our current application domain the data analysis component analyzes transportation 
schedules and records features that may be of interest to the user. 
Propositions such as ?p2 and ?pl are represented in a RQFOL (first-order logic with restricted 
quantification). RQFOL has been used for representing the meaning of natural language queries 
• involving Complex referring expressions \[Webber1983, Woods1983\]. In addition to providing a pew- 
• erful, compositional representation scheme for the complex descriptions occurring in our domain, 
~The tasks are called logical tasks since they do not presuppose a particular graphic design. All design decisions 
such as the type 0f graphic (e.g., a bar chart) or specific properties of a graphic (e.g., the choice of horizontal as 
opposed to vertical bars) are made by SAGE. 
4We recognize that the intended effect of the plan may not be achieved. Dealing with plan failure is an area of 
future research. 
5By convention, symbols beginning with $ are internal identifiers, and symbols beginning with ? are plan variables. 
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(define (action Recommend-act) 
:parameters (?p2) 
:primitive nil 
:effect 
((accepted-recommendation SUser ?p2)) 
(define decomposition Recommend-act) 
:constraints 
((Motivate ?pl ?p2) 
(Proposition ?pl ?main-predl ?refsl) 
(Proposition ?p2 ?main-pred2 ?refs2)) 
:steps 
((Recommend ?main-pred2 ?refs2) 
(Convey-relation Motivation ?pl ?p2) 
(Assert ?main-predl ?refsl))) 
• Figure 2: Plan Operator for •Discourse Strategy 
RQFOL distinguishes information about discourse referents from the main predication of an ex- 
pression. For example, the Proposition plan constraint of the operator in Figure 2, makes use of the 
RQFOL representation of ?pl to extract information with which to instantiate the plan variables 
?main-predl and ?refsl with the main predication of ?pl and a list describing the discourse entities 
\[Webber1983\] evoked or accessed by use of ?main-predl, respectively. (The significance to presen- 
tation generation of the distinction between the main predication and information about discourse 
referents is discussed ill \[Green et M.1998\].) 
The step of the operator shown in Figure 2 underlying (3)ii is an Assert action. In general, 
Assert(?prop, ?refs) is defined as the System asserts ?prop to the User, where ?refs is a list specifying 
all discourse entities evoked or accessed by use of ?lnvp. 6 Discourse entities are specified in the 
list either by an internal identifier (an identifier referring to a database object) or by descriptions 
stated as RQFOL expressions. 
For example, consider the Assert action underlying (3)ii~ which can be represented as follows: 7 
Assert(serves($RI , d2),- 
(($RI, ?dl), 
(d2, (for the c:city(c) has-p0pulation(c,d3))) 
(d3, (for the y:int(y) max(y,d4))), 
(d4, (for set z:int(z) has-population(d5, z))), 
(d5, (for all w:city(w) in(w,$SOMERSET))) 
($SOMERSET, ?d6))) 
The variable ?prop has been instantiated with serves($RI, d2), where SRIand d2 are discourse 
entities; the variable ?refs is instantiated with a list specifying six discourse entities: 
• SRI, a discourse entity (identified by the internal identifier of the database Object which it is intended 
to represent) representing Realtors Inc.,8 " 
• d2, described as the city with a population of d3, 
• d3, described as the maximum value of d4, 
• d4, described as a set of population values derived from dS, 
• d5, described as all cities in $SOMERSET, and 
• $SOMERSET, the discourse entity/databas e object representing Somerset County. 
6The order Of list elements in ?re\]s is used to represent quantifier scope. 
TFor readability, the example is presented in RQFOL-style syntax rather than the LISP-style syntax actually used 
in our implementation. 
8The unbound variables ?dl and ?d6 associated with $RI a~ld $SOMERSET indicate that no descriptions have 
been associated with them by the planner. 
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(define •(action Assert) 
:parameters (?prop ?refs) 
:primitive nil 
:effect 
((bel SUser ?prop ?refs))) 
(define (decomposition Assert) 
:constraints 
((get-id-list ?prop ?refs ?id-list) 
(get-desc-list ?prop ?refs ?desc-list)) 
:steps 
((Predicate ?prop) 
(forall ?id in ?id-list 
(hctivate-ko ?id)) 
(forall ?desc in ?desc-list 
(Activate-as ?desc ?refs)))) 
Figure 3: Plan Operator for Assert 
• Figure 3 shows the definition of an abstract Assert action and a simplified version of its de- 
composition. An Assert may be decomposed into three types of subactions. Predicate is used to 
describe an event independently of th e things that play a role in that event.Activate-ko is a primi- 
tive action used to refer to an object,•i.e., this corresponds to the referential use of a description. 
To achieve the effect of this action, the text and graphics generators are free to select an3, device 
that will enable the user to identify the object (subject to pragmatically appropriate identification 
constraints \[Appelt and Kronfeld1987\]). In other words, since the function of the description is 
purely referential, its content does not contribute directly to the presentation's goals and thus is 
not represented in the plan •. Activate-as is used to refer to a discourse entity as the object fitting the 
description provided, i.e., tliiS corresponds to the attributive use of a description. An Activate-as 
may itself be decomposed into these three types of subactions. 
During hierachical planning, the constraints of the Assert decomposition operator (shown in 
Figure 3) are used to instantiate the plan variables lid-list and ?desc.list. In the forall step of the 
operator, :an Aetivate-ko and Activate-as action is created for each element of ?id-listand ?desc- 
list, respectively. E.g., for the Assert shown above representing (3)ii, the ?id'list would contain the 
identifiers SRI and $SOMERSET, and ?desc-list would include the descriptions of d2 through d5. 
Then, the Assert shown above would be partly decomposed into attributive and referential com- 
municative actions as follows: SRIis the object of an Activate-ko act, and d2 is decomposed into an 
Activate-as act describing d2, which in turn is decomposed into an Activate-as describing d3, and so 
on, ending with an Activate-ko to enable the audience to identify $SOMERSET. In general, a com- 
plex attributive description may contain one or more Activate-ko acts. That is, our representation 
scheme supports the composition of descriptions for attributive use from subcomponents whose use 
may be attributive or referential. Thus, in this example, $SOMERSET could be described in a 
number of ways, e.g., Somerset County or the county on the eastern side of Westmoreland County. 
To summarize the process of generating attributive descriptions in our approach, discourse 
strategies such as Recommend-act (shown in Figure 2) determine content selection as well as whether 
the selected information will be presented as part of the main predication or as part of all attribu- 
tive description. The ill0cutionary act operators (e.g., Assert) and Activate-as operator further. 
decompose any descriptions into Activate-as and Activate-ko acts. •Thus, the •system's intentions 
are represented in the presentation plan, enabling appropriate text and graphics to be generated. 
For •example, because the information associated with d2 (the city with the largest population in 
Somerset county) is part of the above plan, the graphic generator will attempt to produce a graphic 
such as (e) in Figure 1 that will enable the user to see that the agency serving the town with the 
largest population is •Realtors Inc. Without such a specification in the plan, a graphic might be 
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designed showing only that Realtors Inc. serves Berlin, or worse., that Realtors Inc. serves the 
city with the worst pollution in Somerset County. (For examples of how different Communicative 
intentions can be distinguished in graphics see \[Green et a1.1998\].) 
6 Related Work 
\ 
\[Kronfeld1986, Kronfeld1990\] distinguishes three independent aspects of the referential-attributive 
distinction, discusses the significance of the distinction for a computational model of reference, 
and describes how attributive descriptions may result in conversational implicatures \[Grice1975\]. 
The implications of the referential-attributive distinction for centering theory are discussed in 
\[Grosz et a1.1983\]. \[Appelt and Kronfeld1987\] provides a formal theory that derives the effects 
of referring actions. 
Previous •integrated text and graphi c generation systems, e.g., \[Fasciano and Lapalme1996, 
Feiner and McKeown1991, Maybury1991, Wahlster et a1.1993\] have not attempted to perform task- 
based design of graphics as in our. approach. Previous work on natural language reference in 
multimedia generation• \[Andre and Rist1994, McKeown et a1.1992\] has focused on coordination of 
pictoria ! and textual referencesto concrete objectsand to actions to be performed o~ ghe objects, 
and on generating references to the presentation itself. Previous work on reference in sentence gen- 
eration, e.g., \[Appelt1985, Dale1992, Dale and Reiter1995, Heeman and Hirst1995, Horacek1997, 
Stone and Doran1997\], has not addressed the referential-attributive distinction. \[Elhadad1992\] 
describes a representation scheme for specifying complex noun phrases, in which a set can be 
described either by its extension or intension. However, tiffs distinction .is independent of the 
referential-attributive distinction,• since the same noun phrase can be used with either intention. 
7 Conclusion 
We have described a media-independent, compositional, plan-based approach to generating attribu- 
tive descriptions for use in integrated text and graphics generation. An attributive descrip.tion's 
main function is to convey information directly contributing to the communicative goals of a dis- 
course. In our architecture, uses of referential and attributive descriptions are represented as two 
distinct types of communicative acts ina media-independent plan. It is particularly important 
to distinguish the two types of acts, since they have different consequence s for.dialogue followup 
behavior, text generation, and graphic design. 
8 Acknowledgments 
This project was supported by DARPA, contract DAA-1593K0005. 

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