INTRODUCING MAXIMAL VARIATION 
IN TEXT PLANNING FOR SMALL DOMAINS 
Erwin Marsi * 
Department of Language and Speech 
University of Nijmegen 
Abstract 
• This work describes a method for text planning that is suitable to small domains like 
train table information. Our aim is to introduce maximal variation in the packaging of 
• information and in the linear order of its presentation. To this end, we regard text planning 
as a goal-driven process that dynamically constructs a text plan. The goal is a state where all 
information in the input is shared with the user; the means to achieve this goal are utterances. 
The application of utterances is limited by constraints that refer to the user's current state of 
knowledge. This approach to text planning can be conven!ently implemented as a Functional 
Unification Grammar. In addition, we show how optional or inferable information can be 
accountedfor, how focus can be distributed, and how the generation of anaphoric expressions 
can be constrained by looking at the form and content of a previous utterance. 
1 Introduction 
This work on text planning is part of a project that is concerned with investigating Dutch 
prosody by implementing a concept-to-speech system. The project focuses on the prosodic 
module, which predicts the pitch accents and the prosodic boundaries of an utterance on the 
basis •of its semantic and syntactic •structure and its discourse context. The key idea is that a 
natural language generator, as opposed to a parser, generates extensive and reliable information 
about the liriguistic structure of an utterance, and is therefore particularly suitable to provide 
input to the prosodic •module. This approach requires at least two things from the generator. 
First, it should generate all information that the prosodic module needs for deriving the prosodic 
structure of an utterance. Second, it should generate as much variation as• possible, in order 
to put the prosodic module to the test. Given a conventional architecture consisting of a text 
planner followed by a surface generator, these requirements affect the text planner. For instance, 
it should keep track of the information status of concepts, because the distinction between old 
and new information is important for pitch accent placement. With respect to the second 
requirement, it should be able to paraphrase one and the same conceptual structure as different 
semantic structures, which are in turn realized as different •sentences by the surface generator. 
• This paper describes a text planner that meets these requirements. It is described on the 
basis of an application of concept-to-speech in which train table information is taken as input to 
generate a spoken description, in Dutch, of how to get from one placeto another by train. The 
approach, however, is easily adaptable to similar domains. Since we are primarily interested 
in generating linguistically rich and maximally varied input for the prosodic module, the text 
planner is rather uncomplicated and ignores many other aspects of text planning like rhetorical 
"Thanks to Peter-Arno Coppen, Wire Claassen, Carlos Gussenhoven, and two anonymous reviewers for their 
useful comments and corrections. 
58 
ii 
ii 
ii 
J 
~EPT 
SECTIONS 
ROUTE 
"CONCEPT 
DATA 
NEXT 
SECTION 
"DEP-PLACE 
DEP-TIME 
ARR-PLACE 
ARR-TIME 
CONVEYANCE 
DIRECTION 
PLATFORM 
"CONCEPT SECTION 
"DEpoPLACE 
DEP-TIME 
ARR-PLACE 
DATA ARR-TIME 
CONVEYANCE 
DIRECTION 
PLATFORM 
Nijmegen 
1~:08 
's-Hertogenboset~ 
1~:38 
- sneltrein 
Roosendaal 
4b 
"s-Hertogenbosch 
12:4Z 
G elderThalsen 
1~:59 
stoptrein 
Utrecht eentraal, station 
3b 
• Figure 1: Example of an input structure 
structuring of the text or tailoring information to the user. In fact, there is no real dialogue with 
the user in the sense that the system is capable •of reacting on feedback from the user. Also, 
efficiency considerations (real time behaviour) have not played a role. The interesting points, 
however, are that the text planner employs a constraint-based approach to produce variation 
and that its implementation is completely grammar-based within the framework of Functional 
Unification Grammar. 
2• A Functional Unification Grammar for text planning 
2.1 Input structures 
The input to the text planner comes from an existing train travel information system. In 
response to a query typed by the user, it outputs travel information in a tabular format. This 
information is mapped to a feature description (FD) of hierarchically structured concepts in a 
straightforward way. For instance, the FD in Figure 1 represents a journey with one change. 
The top concept, representing •the whole journey from departure place to arrival place, is called 
ROUTE. 1 It is composed of one or more SECTION nodes, each of which represents a partial 
journey- from one place to another. A section node is accompanied by information about the 
place and time of departure, the place and time of arrival, the type of conveyance, its direction, 
and the platform it leaves from. Notice that the attribute NEXT serves as a link to the subsequent 
section. 
2.2 Text planning grammar 
Text planning is regarded as the process of mapping the input structure to a sequence of 
semantic structures, which will ultimately be realized as spoken utterances. Evidently, not 
lit also contains information like the total amount of traveling time and the number of changes, which is used 
to generate a summary of the journey. This option will be ignored here. 
69 
-CONCEPT SECTION 
DONE ( 1" UNITS DONE ) 
CSET == (UNITS). 
"CONCEPT UNIT 
• DATA ( I" I" DATA ) 
"DEP-PLACE 
DEP-TIME 
UNF~ ARR-PLACE 
BMB ARRoTIME 
CONVEYANCE 
DIRECTION 
PLATFORM 
I :tuaif- DONE 
\[CSET + (NEXT.) 
\[NEXT\[ CONCEPT 
UNKNOWN" 
UNKNOWN 
UNKNOWN 
UNKNOWN 
UNKNOWN 
UNKNOWN 
UNKNOWN 
sECTION\] 1 
Figure 2: Grammar alternative for a section 
all the information in the input can be expressed in a single utterance, so the text planner 
must divide it into smaller packages. The information within a package should be coherent and 
the linear order of the packages should make sense. For instance, it is quite odd to start the 
description of a section with the arrival place and arrival time, that is, without mentioning the 
departure place and departure time first. Ruling out certain ways of information packaging is 
of course a matter of common sense; it is always possible to come up with a context in which 
a very marked order of presentation is acceptable. The obvious solution is to use one or more 
templates that prescribe acceptable ways of presenting the information. However, as explained 
above, our goal is to generate as much variation as possible. Using just a limited number of 
templates wduld severely restrict the amount of variation at the level of text planning. To 
obtain more variation, one has to Create an extensive list of templates, which accounts for all 
possible ways of pafJ~aging and linear ordering of information. 
The alternative is to adopt a dynamic approach to text planning, and to consider it as 
an attempt to achieve a particular goal under certain constraints (Hovy 1991). The goal is a 
transfer of all available information, i.e. a state where the user knows all the information that 
is in the input structure. This does not imply, however, that all data have to be • explicitly 
expressed, because the listener may infer some of it from the situational context or from the 
previous discourse. For example, the departure place may be inferred, because it is the arrival 
place of th e previous section. The means to achieve this goal are utterances. Generating a 
semantic structure for an utterance may be considered as performing a speech act that alters 
the user's state of knowledge (Cohen and Perrault 1979). According to this view, the use of 
a certain utterance is limited by constraints referring to the user's current state of knowledge, 
and the form and content of •previous utterances. Within the boundaries of these constraints, 
planning is assumed to be a dynamic process directed by random choices. As a result, the 
output of the planner will vary considerably from one run to another. Thus, the text planner 
is not designed to generate a plan that will eventually transfer the information to the user 
• optimally, but instead to generate as many plans as possible, which nevertheless transfer the 
information in an acceptabIe way. 70 
!1 
I 
I 
I 
i 
'!1 
! 
:i I 
! 
i| 
"CONCEPT UNIT 
'DEP=PLACE UNKNOWN I 
ARR-PLACE UNKNO'VN / 
BMB CONVEYANCE UNKNOWN I 
\[.DIRECTION UNKNOWNJ 
"Go .from (DEP-PLACE) tO (ARR-PLACE) 
with the (CONVEYANCE) towards (DIRECTION)' 
"CONCEPT UNIT 
DATA 
DONE 
NEXT 
• BMB 
(I" I" DATA) 
'(I" I" DONE) 
"DEP-PLACE KNOWN 
ARR-PLACE KNOWN 
DEP-TIME (I" I" I" BMB DEP-TIME) 
ARR-TIME (~" I" I" BMB ARR-TIME) 
CONVEYANCE KNOWN 
DIRECTION KNOWN 
PLATFORM " (~" 1" 1" BMB PLATFORM). 
Figure 3: One of the grammar alternatives for a unit 
The text planner is implemented as a Functional Unification Grammar (Kay 1984) in 
FUF (Elhadad 1993). The grammar is a feature description that consists of a number of alter- 
natives, most of which represent an utterance with its constraints on application, its semantic 
structure and its effect on the user's knowledge. The process of text pl .anning is a step-wise uni- 
fication of the input with the grammar. The control mechanism of FUF traverses all concepts 
in the input structure (i.e. sub-FD's that contain the attribute CONCEPT), unifying them with 
suitable alternatives of the grammar. During this process, the input structure is enriched with 
new concepts, semantic structures and updates of the user's knowledge state. 
We will trace this process on the basis of a simplified example. Suppose we take the FD in 
Figure 1 as input. Each SECTION concept in the input is unified with a corresponding grammar 
alternative. The grammar alternative for SECTION (see Figure 2) adds a feature UNITS that is 
used to store a number of nodes of type UNIT, corresponding to the utterances that together 
describe a section. A section typically contains between two and six units. A unit has a feature 
BMB, shorthand for 'belief-mutual-belief', which represents the text planner's belief about the 
current knowledge shared with the user. The alternative for SECTION initializes the knowledge 
state for its first unit: it is assumed that initially all information is unknown. The remaining 
features will be explained later on. 
The grammar contains many different alternatives for UNIT, of which the one in Figure 3 
is an example. The value of the BMB is best viewed as a condition on the applicability of this 
alternative. For the current example, it states that the departure place, departure time and 
conveyance must be unknown. Notice thati due to the nature of unification, the condition is 
indifferent with respect to the status of other data; they can be either known or unknown. If 
the condition succeeds, the speech act under ACT can be performed, which amounts to sending 
a semantic structure to the surface generator. The string template shown as the value Of ACT 
is for expository reasons only; the value is actually an FD that is the semantic structure for an 
utterance. Semantic structures will be discussed later On. The slots in the template are filled 
by reference to the relevant values under DATA, which is the reason why this attribute is shared 
71 
-CONCEPT UNIT 
"DEP-PLACE 
DEP-TIME 
ARR-PLACE 
BMB ARR-TIME " 
CONVEYANCE 
. DIRECTION 
PLATFORM 
DONE TRUE 
KNOWN" 
KNOWN 
KNOWN 
KNOWN 
KNOWN 
KNOWN 
KNOWN 
Figure 4: The special unit that states the termination condition 
between a unit and a section (cf: Figure 2) and between units (cf. Figure 3). 2 Now performing a 
speech act alters the knowledge state, which is modeled by the fact that in the subsequent unit 
the values of the attributes DEP-PLACE, ARR-PLACE, CONVEYANCE, and DIRECTION become 
known. The state of the other data is shared with the previous BMB, implying that their status 
remains unaffected by the current speech act. 
The expansion of a unit into a speech act and a next unit is a recursive process. It continues 
until BMB reaches the point where all data have become known. This termination condition is 
modeled by a special unit that has neither a speech act nor a NEXT attribute; see Figure 4. It 
does, however, provide the attribute DONE with its value TRUE, and because this value is shared 
between subsequent units as well as between a section and its first unit, it means that in the 
section node the attribute DONE becomes TRUE too. This in turn, triggers the alternatives 3 in 
Figure 2, which had been frozen by means of the special option :wait until the feature DONE 
had received a value. FUF tries the alternatives in the order they are given in the grammar. 
The first alternative succeeds if no more sections are given in the input, i.e. this was the last 
section of the route. Otherwise, the second alternative is taken, which forces processing of the 
next sectionJ 
The important thing to notice is that when the next unit must be added, there are in 
general multiple units whose conditions are compatible with the current knowledge state. At 
such points, the random choice of a unit introduces the variation that was sought after. However, 
not every choice will lead to a solution, Causing FUF to backtrack and revise its choice of units. 
Thus, the text planner can actually be Considered a planner in the AI sense of the wordas a 
program that traverses a search space (a network of connected units) for a path (a sequence of 
units with associated speech acts and knowledge Updates) that satisfies its goal (a state where 
the planner believes that all data is shared with the user). 
Figure 5 shows an example of a part of the output of the text planner based on the input i n 
2The fact that two attributes share the same value is expressed by means of path. For instance, the path 
~" 1" DATA > that is the value of DATA means: go Up two levels (i.e. skipping the attributes DATA and UNITS) 
and from there follow the attribute DATA to arrive at the intended value. This value is not present yet in the 
grammar alternative 0f Figure 2, but will be present in the input structure it is unified with. 
aAlternatives (disjunctions) in the grammar are indicated by braces. 
4The :wait option fo/'ces goal-freezing and is one of the ways in which FUF extends the FUG formalism it 
is based on. Another extension is exemplified by the special attribute CSET. By default, the unifier identifies 
COnstituents (i.e. sub-FD's in the input that need to be unified with the grammar) by the presence of a special 
attribute (CONCEPT in our case) and traverses these constituents in a top-down breadth-first manner. The 
CSET attribute enables the grammar writer to overrule this default and explicitly specify the constituents. This 
options is used to force processing of al units (by CSET =----(UNITS) ) before the next section is processes (by CSET 
-\[-(NEXT)). See (E!hadad 1993): 
72 
! 
! 
!!| 
i 
I 
iii 
"CONCEPT 
SECTIONS 
ROUTE 
"CONCEPT 
JN\] 
SECTION 
CONCEPT UNIT 
DONE ( 1` NEXT DONE ) 
"DEP-PLACE UNKNOWN" 
DEP-TIME . UNKNOWN 
ARR-PLACE UNKNOWN 
BMB ARR-TIME UNKNOWN 
CONVEYANCE UNKNOWN 
DIRECTION UNKNOWN 
PLATFORM • UNKNOWN 
CAT S . " ~ "I 
ACT \[SEM ~U gaat van Nijmegen naar s-Hertogenbosch 
met de sneltrein richtin 9 Roosendaal." 
"CONCEPT UNIT 
DONE ( I" NEXT DONE ) 
• "DEP-PLAcE KNOWN 
DEP-TIME ( 1" 1` 1` BMB DEP-TIME ) 
ARR-PLACE 
VIB ARR-TIME B\] 
CONVEYANCE 
DIRECTION 
PLATFORM 
CAT S 
ACT \[SEM "Die vertrekt vana\] perrron ~b om twaalf uur acht.' 
"CONCEPT UNIT 
DONE ( 1` NEXT DONE) 
-DEP-PLACE 
DEP-TIME 
• ARR-PLACE 
BMB ARK-TIME 
CONVEYANCE 
DIRECTION 
PLATFORM 
KNOWN 
( 1` 1` 1` BMB ARR-TIME ) 
KNOWN • 
KNOWN 
1` 1` 1` BMB PLATFORM 
KNOWN 
KNOWN 
KNOWN 
( ~ ~ ~ BMB ARK-TIME 
•KNOWN 
KNOWN 
KNOWN 
CAT S " " " 
ACT \[SEM "U arriveert in 's.Hertogenbosch \[ 
om twaalf uur aehtendertig." 
"CONCEPT UNIT " 
DONE TRUE 
BMB 
"DEP-PLACE KNOWN" 
DEP-TIME KNOWN 
ARR-PLACE KNOWN 
ARR-TIME KNOWN 
CONVEYANCE KNOWN 
DIRECTION KNOWN 
PLATFORM KNOWN 
N EX~ 
NEXT ... 
Figure 5: A part of the.output that corresponds to the description of one section with three 
utterances. See example (3) for a gloss of the Dutch sentences. 
73 
Figure 15. Notice that in the third unit the arrival place is repeated, although it was already 
mentioned in the first unit. This is possible because the grammar alternative for the third 
unit requires the departure time to be unknown, but does not constrain the value for arrival 
• place. Therefore, it can be applied to introduce the arrival time only, • or to introduce the arrival 
place as well. Either way, the arrival place is known after application of the unit. However, 
every units, with the exception of the termination unit, requires at least one piece of data to be 
unknown, since otherwise its application would be superfluous. 
The planning grammar presented so far is simplified; the one actually used has a number 
of extensions. For instance, the assumption that some information is optional is modeled by 
relaxing the termination condition. • That is, if the feature \[PLATFORM KNOWN\] is removed 
from the FD in Figure 4, then processing of a section may finish without making mention of 
the platform. Furthermore, the assumption that the place of departure is inferable, since it is 
the arrival place of the previous section, is implemented by forcing the departure place to be 
known in the first unit of a non:initial section. Two othe r extensions, for generating anaphoric 
expressions and discourse markers, will be discussed next. 
2.3 Semantic structures 
As mentioned earlier, the value of an ACT attribute is not a string template, but an FD that is 
the semantic structure for an utterance. An example is given in Figure 6. 6 It ispassed on to a 
surface generator for Dutch that is similar to the SURGE surface generator for English (Elhadad 
and Robin 1996)/ Notice how the lemmas for participants and circumstances are instantiated 
by means of paths that refer to the relevant values within the unit's DATA feature, s 
Figure 6 also illustrates the distribution of focus. A constituent that is focused is presented as 
important to the listener (as opposed to unf0cused material that is presented as less important to 
the listener). In general, information of which the speaker assumes that the listener is unfamiliar 
with is unfocused, and vice versa. 9 The distinction has repercussions for both syntactic and 
prosodic realization. Focus affects the syntactic structure, because it is used by the surface 
generator to determine the word order of an utterance. In particular, it will strive for a canonical 
word order with unfocused material at the start of the utterance and focused material at the 
end. Focus ,affects the prosodic structure, because focused material will be marked by at least 
• one pitch accent. For current purposes, this means that • checking the value of BMB provides a 
convenient way to determine if something is focused or not. This check is implemented as the 
option between parentheses in Figure 6. It states that if the arrival place is known, then its 
realization must be unfocused. However, if the arrival place is unknown, the option fails and 
the value for FOCUS is left unspecified. This interacts with the default assumption about focus 
made by the surface generator: Content words are focused,: while function words are unfocused. 
Hence, the text planner can limit itself to the exceptions, like the aforementioned case where 
the departure place is realized as a content word, but is nonetheless unfocused. Likewise, there 
is no need to explicitly specify that •the Instrument is •focused, or the Agent is unfocused. 
In addition to the distribution of focus, the text planner is also responsible for generating 
5The features DATA, CSET, FC, as well as the second section, were left out to save space. 
. 6<:1`7 DATA. ARRLPLACE~> is an abbreviation of <1" 1" 1" 1" 1" 1" 1" DATA ARR-PLACE> 
ZThis generator, called SEM2SYN, is a reusable surface generator for Dutch implemented in FUF (Marsi 
1998). Its use is not limited to the present domain of travel descriptions. It has also been used to generate 
botanical descriptions of plants. 
SAt present, the tex.t planner performs lexical choice, and is therefore responsible for variation at lexical level. 
This is not a not the only option however, since lexical choice might as well be performed in a separate module. 
9The focused versus unfocused distinction does not always coincide with the known versus unknown distinction. 
For example, old. or know n informat~oa may be focused, to obtain a contrastive effect. 74 
! 
"CONCEPT 
BMB . . . 
DATA . . . 
"CAT 
ACT 
UNIT 
S 
PREDICATE \[ SYNSEM 
NEXT ... 
"LEMMA 9n~n 
• "AGENT \[ SYNSEM I PERSON SECOND I 
I'LE'"A Ct' VATA ARR-PLACE) 1/ 
PARTIC \[PROPER TRUE // GOAL \[ SYNSEM . " " 
• L\LFOCUS FALSE " j/jj 
ILEMMA (I.7 DATA i 
CIRCUM I INSTRUMENT \]II(~ 6 BMB ARR-PLACE) KNOWNI~ I 
L~k\[FOCUS FALSE . \]\]j 
Figure 6: A unit containing the semantic structure for the utterance U gaat naar 
met de <conveyance>. 'You go to <arr-place> with the <conveyance>.' 
< arr-place> 
anaphoric expressions. The range of possible anaphoric expressions within the present domain 
is quite small. First, the listener is situationally evoked and is always referred to by a personal 
pronoun. Second, the conveyance may be referred to by a relative pronoun if it has been 
mentioned before. Third,• a departure place, arrival place, direction or platform may be referred 
to by a locative anaphoric• adverb. The latter type of reference is less trivial, because its use 
is restricted by word order. A case in point is (1) versus (2). The anaphoric expression daar 
('there') is most naturally interpreted as referring to the place that was most recently mentioned. 
This leads to the intended interpretation (i.e. the departure place) in (l-b), but to a confusing 
or even unintended interpretation (i.e. the direction of the conveyance) in (2-b). Thus, in order 
to generate adequate anaphoric expressions of place, the text planner must keep track of the 
most recently mentioned place. 
(1) a. U neemt de sneltrein richting Roosendaal in Nijmegeni. 
you take the train towards Roosenda~l in Nijmegen 
• b. Daari vertrekt u om 12:08 van perron ~b. "" 
there leave you at !2:08 from platform 4b 
(2) a. U neemt in Nijmegen de sneltrein richting Roosendaali. 
you take in Nijmegen the train towards Roosendaal. 
b. *Daari vertrekt u om 12:08 van perron Jb. 
there leave you at 12:08 from platform 4b 
This is implemented by means of a feature FC 1° that tells a unit what the most recently men- 
tioned items of type HUMAN, PLACE and OBJECT are. This way, a unit can consult the content Of 
FC to decide if an anaphoric expression can be used in its accompanying utterance. Depending 
on the content and word order of its utterance, a unit projects similar information to the FC 
I°FC stands for 'forward centers', because its use shows some resemblance to the notion of a set of forward 
centers in centering theory (Grosz, Joshi, and Weinstein 1995). However, the text planner is certainly not meant 
to be an implementation of centering theory. 
75 
-CONCEPT UNIT 
FC\]. PLACE DEP-PLACE 
ACT "Daar vertrekt u om 12:08 van perron ~b ~ 
CONCEPT UNIT 
/ \[HUMAN LISTENER 
NEXT I FC I PLACE PLATFORM 
L LOBJECT CONVEYANCE 
Figure 7: Example of the use of FC. See example (la) for a gloss of the Dutch sentence. 
feature of the next unit. An example is given in Figure 7. 
The difference between the features BMB and FC is that the former tells us whether something 
is already known (either because it was mentioned in one of the previous sentences or because 
it could be inferred), whereas the latter tells us whether something was mentioned in the latest 
utterance. 
This approach poses an interesting question regarding word order: is word orderdetermined 
by the text planner, by the surface generator, or perhaps by both? One point • of view is that 
a semantic structure is passed on to the surface generator, which determines the word order, 
which in turn determines the FC a unit projects to the next unit. This assumes that there 
is feedback from surface generator to text planner and that generation proceeds 'depth-first' 
(i.e. plan the first utterance, realize the first• utterance, plan the second utterance, realize the 
second utterance, etc.) An alternative point of view is that the semantic structure contains 
restrictions on word order (like 'mention the departure place last'), depending on the FC a unit 
projects to the next unit. This requires no feedback and assumes that the generation process 
is 'breadth-first' (i.e. planning all utterances before sending them to the surface generator). So 
far, we have adopted the latter approach, • because it less complicated, both in concept and in 
implementation. 
Finally, the text planner also inserts discourse markers. For the time being, this is just 
a provisional solution to improve the quality of the output; • the implementation is not based 
on any theory. Since the nature of the domain is a small narrative in which the sections are 
described• in •chronological order, temporal continuity markers are suitable in most cases. For 
example, the first unit of non-initial section may add a temporal continuation marker like next, • 
then, after that etc. It would be interesting to explore the possibilities of a more principled 
account of discourse markers, e.g. by using rhetorical relations as in (Hovy 1991). 
• 2.4 Final output 
(3) gives an example of a travel description in Dutch, generated by the Combination of text 
planner and the surface generator, and based on the input in Figure 1. 
(3) a. U #aat van Nijmegen naar 's-Hertogenbosch met de sneltrein richting 
you go from Nijmegen to 's-Hertogenbosch with the express-train towards 
Roosendaal. 
Roosendaal 
'You take the Roosendaal express train to 's-Hertogenbosch.' 
b. Die vertrekt van perron ~b om twaalf uur acht. 
that leaves from platform 4b at twelve hour eight 
'Which •leaves from platform 4b at 12.08' 
76 
I 
I 
I 
I 
I 
I 
I 
I 
I 
C. • 
d. • 
e. 
f. 
U arriveert in 's-Hertogenbosch om twaalf uur achtendertig. 
You arrive in 's-Hertogenbosch at twelve hour thirty-eight 
'Which gets you to 's-Hertogenbosch at 12.38' 
Vervolgens neemt u. daar de stoptrein richting Utrecht Centraal Station. 
next take you there the local-train towards Utrecht Central Station 
'Next, take the local train to Utrecht Central Station.' 
Die vertrekt in 's-Hertogenbosch van perron 3b om twaalf uur tweeenveertig. 
that leaves in 's-Hertogenbosch from platform 3b at twelve hour fortytwo 
• 'Which leaves in 's-Hertogenbosch from platform 3b at 12.42.' 
Dan bent u in Geldermalsen om twaalf uur negenenvijftig. 
then are you •in Geldermalsen at twelve hour fiftynine • 
'Which gets you to Geldermalsen at 12.597 
3 Summary 
We have described a simple method for text planning that is suitable to small domains like travel 
information. Our aim was to introduce maximal variation in the packaging of information and 
in the linear order of its presentation. To this end, it proved useful to view text planning as a 
goal-driven process, in Which utterances are used to alter the knowledge state of the user, and 
their use is restricted only by constraints that refer to the user's knowledge state. This can be 
• conveniently implemented as a Functional Unification Grammar. In addition, we showed how 
optional or inferable information can be accounted for, how focus is .distributed, and how the 
generation of anaphoric expressions can be •constrained by the form and content of the latest 
utterance. Future work may address the necessity of feedback from surface generator to text 
planner, and the incorporation of a more •principled account of generating discourse markers. 
Furthermore, an evaluation 0f the output with real users would be desirable. 

References 
Cohen, P. and C. Perrault (1979). Elements of a plan-based theory of speech acts. Cognitive 
Science (3), 177-212. 
Elhadad, M. (1993). FUF: The Universal Unifier - User Manual, version 5.2. New York. 
Technical Report CUCS-038-91. 
Elhadad, M. and J. Robin (1996). An overview of SURGE: A reusable comprehensive syntactic 
realization component. Technical Report 96-03, Ben Gurion University, Department of 
computer Science, Beer Sheva, Israel. 
Grosz, B. J., A. K. Joshi, and S. Weinstein (1995). Centering: a framework for modeling the 
local coherence of discourse. Computational Linguistics 21 (2), 203-225. 
Hovy, E. H. •(1991). Approaches to the planning of coherent text. In W. Swartout and 
W. Mann (Eds.), Natural Language Generation in Artificial Intelligence and Computa- 
tional Linguistics, pp. 83-102. Boston: Kluwer Academics Publishers. 
Kay, M. (1984). Functional unification grammar: A formalism for machine translation. In 
Proceedings of COLING-84, pp. 75-78. ACL, Stanford University. 
Marsi, E. (1998). A reusable syntactic generator for Dutch. In P.-A. Coppen, H. V. Halteren, 
and L. Teunissen (Eds.), Proceedin9s o\]7C7LIN9Z Nijmegen. 
