Segregatory Coordination and Ellipsis in Text Generation 
James Shaw 
Dept. of Computer Science 
Columbia University 
New York, NY 10027, USA 
shaw@cs.columbia.edu 
Abstract 
In this paper, we provide an account of how 
to generate sentences with coordination con- 
structions from clause-sized semantic represen- 
tations. An algorithm is developed and various 
examples from linguistic literature will be used 
to demonstrate that the algorithm does its job 
well. 
1 Introduction 
The linguistic literature has described numer- 
ous coordination phenomena (Gleitman, 1965; 
Ross, 1967; Neijt, 1979; Quirk et al., 1985; van 
Oirsouw, 1987; Steedman, 1990; Pollard and 
Sag, 1994; Carpenter, 1998). We will not ad- 
dress common problems associated with pars- 
ing, such as disambiguation and construction of 
syntactic structures from a string. Instead, we 
show how to generate sentences with complex 
coordinate constructions starting from seman- 
tic representations. We have divided the pro- 
cess of generating coordination expressions into 
two major tasks, identifying recurring elements 
in the conjoined semantic structure and delet- 
ing redundant elements using syntactic informa- 
tion. Using this model, we are able to handle 
coordination phenomenon uniformly, including 
difficult cases such as non-constituent coordina- 
tion. 
In this paper, we are specifically interested in 
the generation of segregatory coordination con- 
structions. In segregatory coordination, the co- 
ordination of smaller units is logically equivalent 
to coordination of clauses; for example, "John 
likes Mary and Nancy" is logically equivalent 
to "John likes Mary" and "John likes Nancy". 
Other similar conjunction coordination phe- 
nomena, such as combinatory and rhetorical co- 
ordination, are treated differently in text gener- 
ation systems. Since these constructions cannot 
be analyzed as separate clauses, we will define 
them here, but will not describe them further 
in the paper. In combinatory coordination, the 
sentence "Mary and Nancy are sisters." is not 
equivalent to "Mary is a sister." and "Nancy 
is a sister." The coordinator "and" sometimes 
can function as a rhetorical marker as in "The 
train sounded the whistle and \[then\] departed 
the station." 1 
To illustrate the common usage of coordina- 
tion constructions, we will use a system which 
generates reports describing how much work 
each employee has performed in an imaginary 
supermarket human resource department. Gen- 
erating a separate sentence for each tuple in the 
relational database would result in: "John re- 
arranged cereals in Aisle 2 on Monday. John 
rearranged candies in Aisle 2 on Tuesday." A 
system capable of generating segregatory coor- 
'dination construction can produce a shorter sen- 
tence: "John rearranged cereals in Aisle 2 on 
Monday and candies on Tuesday." 
In the next section, we briefly describe the 
architecture of our generation system and the 
modules that handle coordination construction. 
A comparison with related work in text gener- 
ation is presented in Section 3. In Section 4, 
we describe the semantic representation used 
for coordination. An algorithm for carrying 
out segregatory coordination is provided in Sec- 
tion 5 with an example. In Section 6, we will 
analyze various examples taken from linguistic 
literature and describe how they are handled by 
the current algorithm. 
2 Generation Architecture 
Traditional text generation systems contain a 
strategic and a tactical component. The strate- 
gic component determines what to say and the 
order in which to say it while the tactical com- 
ponent determines how to say it. Even though 
1The string enclosed in symbols \[ and \] are deleted 
from the surface expression, but these concepts exist in 
the semantic representation. 
1220 
the strategic component must first decide which 
clauses potentially might be combined, it does 
not have access to lexical and syntactic knowl- 
edge to perform clause combining as the tac- 
tical component does. We have implemented a 
sentence planner, CASPER (Clause Aggregation 
in Sentence PlannER), as the first module in 
the tactical component to handle clause combin- 
ing. The main tasks of the sentence planner are 
clause aggregation, sentence boundary determi- 
nation and paraphrasing decisions based on con- 
text (Wanner and Hovy, 1996; Shaw, 1995). 
The output of the sentence planner is an or- 
dered list of semantic structures each of which 
can be realized as a sentence. A lexical chooser, 
based on a lexicon and the preferences speci- 
fied from the sentence planner, determines the 
lexical items to represent the semantic concepts 
in the representation. The lexicalized result is 
then transformed into a syntactic structure and 
linearized into a string using FUF/SURGE (E1- 
hadad, 1993; Robin, 1995), a realization compo- 
nent based on Functional Unification Grammar 
(Halliday, 1994; Kay, 1984). 
Though every component in the architecture 
contributes to the generation of coordinate con- 
structions, most of the coordination actions take 
place in the sentence planner and the lexical 
chooser. These two modules reflect the two 
main tasks of generating coordination conjunc- 
tion: the sentence planner identifies recurring 
elements among the coordinated propositions, 
and the lexical chooser determines which recur- 
ring elements to delete. The reason for such a 
division is that ellipsis depends on the sequen- 
tial order of the recurring elements at surface 
level. This information is only available after 
syntactic and lexical decisions have been made. 
For example, in "On Monday, John rearranged 
cereals in Aisle 2 and cookies in Aisle 4.", the 
second time PP is deleted, but in "John rear- 
ranged cereals in Aisle 2 and cookies in Aisle 
4 on Monday.", the first time PP is deleted. 2 
CASPER only marks the elements as recurring 
and let the lexical chooser make deletion deci- 
sions later. A more detailed description is pro- 
vided in Section 5. 
2The expanded first example is "On Monday, John 
rearranged cereals in Aisle 2 and \[on Monday\], \[John\] 
\[rearranged\] cookies in Aisle 4." The expanded second 
example is "John rearranged cereals in Aisle 2 \[on Mon- 
day I and \[John\] \[rearranged\] cookies in Aisle 4 on Mon- 
day." 
3 Related Work 
Because sentences with coordination can ex- 
press a lot of information with fewer words, 
many text generation systems have imple- 
mented the generation of coordination with var- 
ious levels of complexities. In earlier systems 
such as EPICURE (Dale, 1992), sentences with 
conjunction are formed in the strategic compo- 
nent as discourse-level optimizations. Current 
systems handle aggregations decisions including 
coordination and lexical aggregation, such as 
transforming propositions into modifiers (adjec- 
tives, prepositional phrases, or relative clauses), 
in a sentence planner (Scott and de Souza, 1990; 
Dalianis and Hovy, 1993; Huang and Fiedler, 
1996; Callaway and Lester, 1997; Shaw, 1998). 
Though other systems have implemented co- 
ordination, their aggregation rules only handle 
simple conjunction inside a syntactic structure, 
such as subject, object, or predicate. In con- 
trast to these localized rules, the staged algo- 
rithm used in CASPER is global in the sense that 
it tries to find the most concise coordination 
structures across all the propositions. In addi- 
tion, a simple heuristic was proposed to avoid 
generating overly complex and potentially am- 
biguous sentences as a result of coordination. 
CASPER also systematically handles ellipsis and 
coordination in prepositional clauses which were 
not addressed before. When multiple proposi- 
tions are combined, the sequential order of the 
propositions is an interesting issue. (Dalianis 
and Hovy, 1993) proposed a domain specific or- 
dering, such as preferring a proposition with an 
animate subject to appear before a proposition 
with an inanimate subject. CASPER sequential- 
izes the propositions according to an order that 
allows the most concise coordination of propo- 
sitions. 
4 The Semantic Representation 
CASPER uses a representation influenced by 
Lexical-Functional Grammar (Kaplan and Bres- 
nan, 1982) and Semantic Structures (Jackend- 
off, 1990). While it would have been natural 
to use thematic roles proposed in Functional 
Grammar, because our realization component, 
FUF/SURGE, uses them, these roles would 
add more complexity into the coordination pro- 
cess. One major task of generating coordina- 
tion expression is identifying identical elements 
in the propositions being combined. In Func- 
1221 
((pred ((pred c-lose) (type EVENT) 
(tense past))) 
(argl ((pred c-name) (type THING) 
(first-name ''John'S))) 
(arg2 ((pred c-laptop) (type THING) 
(specific no) 
(mod ((pred c-expensive) 
(type ATTRIBUTE))))) 
(mod ((pred c-yesterday) 
(type TIME)))) 
Figure 1: Semantic representation for "John 
lost an expensive laptop yesterday." 
A1 re-stocked milk in Aisle 5 on Monday. 
A1 re-stocked coffee in Aisle 2 on Monday. 
A1 re-stocked tea in Aisle 2 on Monday. 
A1 re-stocked bread in Aisle 3 on Friday. 
Figure 2: A sample of input semantic represen- 
tations in surface form. 
tional Grammar, different processes have differ- 
ent names for their thematic roles (e.g., MEN- 
TAL process has role SENSER for agent while 
INTENSIVE process has role IDENTIFIED). 
As a result, identifying identical elements un- 
der various thematic roles requires looking at 
the process first in order to figure out which 
thematic roles should be checked for redun- 
dancy. Compared to Lexical-Functional Gram- 
mar which uses the same feature names, the the- 
matic roles for Functional Grammar makes the 
identifying task more complicated. 
In our representation, the roles for each event 
or state are PRED, ARG1, ARG2, ARG3, and 
MOD. The slot PRED stores the verb concept. 
Depending on the concept in PRED, ARG1, 
ARG2, and ARG3 can take on different the- 
matic roles, such as Actor, Beneficiary, and 
Goal in "John gave Mary a red book yester- 
day." respectively. The optional slot MOD 
stores modifiers of the PRED. It can have one 
or multiple circumstantial elements, including 
MANNER, PLACE, or TIME. Inside each argu- 
ment slot, it too has a MOD slot to store infor- 
mation such as POSSESSOR or ATTRIBUTE. 
An example of the semantic representation is 
provided in Figure 1. 
5 Coordination Algorithm 
We have divided the algorithm into four stages, 
where the first three stages take place in the 
sentence planner and the last stage takes place 
A1 re-stocked coffee in Aisle 2 on Monday. 
A1 re-stocked tea in Aisle 2 on Monday. 
A1 re-stocked milk in Aisle 5 on Monday. 
A1 re-stocked bread in Aisle 3 on Friday. 
Figure 3: Propositions in surface ~rm after Stage 1. 
in the lexical chooser: 
Stage 1: group propositions and order them 
according to their similarities while satisfy- 
ing pragmatic and contextual constraints. 
Stage 2" determine recurring elements in the 
ordered propositions that will be combined. 
Stage 3: create a sentence boundary when the 
combined clause reaches pre-determined 
thresholds. 
Stage 4" determine which recurring elements 
are redundant and should be deleted. 
In the following sections, we provide detail on 
each stage. To illustrate, we use the imaginary 
employee report generation system for a human 
resource department in a supermarket. 
5.1 Group and Order Propositions 
It is desirable to group together propositions 
with similar elements because these elements 
are likely to be inferable and thus redundant 
at surface level and deleted. There are many 
ways to group and order propositions based on 
similarities. For the propositions in Figure 2, 
the semantic representations have the follow- 
ing slots: PRED, ARG1, ARG2, MOD-PLACE, 
and MOD-TIME. To identify which slot has the 
most similarity among its elements, we calcu- 
late the number of distinct elements in each 
slot across the propositions, which we call NDE 
(number of distinct elements). For the purpose 
of generating concise text, the system prefers to 
group propositions which result in as many slots 
with NDE -- 1 as possible. For the propositions 
in Figure 2, both NDEs of PRED and ARG1 
are 1 because all the actions are "re-stock" and 
all the agents are "AI"; the NDE for ARG2 is 4 
because it contains 4 distinct elements: "milk", 
"coffee", "tea", and "bread"; similarly, the NDE 
of MOD-PLACE is 3; the NDE of MOD-TIME 
is 2 ("on Monday" and "on Friday"). 
The algorithm re-orders the propositions by 
sorting the elements in each slots using compar- 
ison operators which can determine that Mon- 
day is smaller than Tuesday, or Aisle 2 is smaller 
than Aisle 4. Starting from the slots with 
largest NDE to the lowest, the algorithm re- 
1222 
((pred c-and) (type LIST) 
(elts 
"(((pred ((prsd "re-stocked") (type EVENT) 
(status RECI/RRING) ) ) 
(arE1 ((pred "AI") (TYPE THING) 
(status RECURRING) ) ) 
(arE2 ((pred "tea") (type THING))) 
(rood ((pred "on") (type TIME) 
(arEl ((pred "Monday") 
(type TIME-THING) ) ) ) ) ) 
((pred ((pred "re-stocked") (type EVENT) 
(status RECURRING) ) ) 
(argl ((pred "AI") (TYPE THING) 
(status RECURRING) ) ) 
(arE2 ((pred "milk") (type THING))) 
(rood ((pred "on") (type TIME) 
(arE1 ((pred "Friday") 
(type TIME-THING) ) ) ) ) ) ) ) ) 
Figure 4: The simplified semantic representation 
for "A1 re-stocked tea on Monday and milk on Fri- 
day." Note: "0 - a list. 
orders the propositions based on the elements of 
each particular slot. In this case, propositions 
will ordered according to their ARG2 first, fol- 
lowed by MOD-PLACE, MOD-TIME, ARG1, 
and PRED. The sorting process will put similar 
propositions adjacent to each other as shown in 
Figure 3. 
5.2 Identify Recurring Elements 
The current algorithm makes its decisions in 
a sequential order and it combines only two 
propositions at any one time. The result propo- 
sition is a semantic representation which repre- 
sents the result of combining the propositions. 
One task of the sentence planner is to find a way 
to combine the next proposition in the ordered 
propositions into the resulting proposition. In 
Stage 2, it is concerned with how many slots 
have distinct values and which slots they are. 
When multiple adjacent propositions have only 
one slot with distinct elements, these proposi- 
tions are 1-distinct. A special optimization can 
be carried out between the 1-distinct proposi- 
tions by conjoining their distinct elements into 
a coordinate structure, such as conjoined verbs, 
nouns, or adjectives. McCawley (McCawley, 
1981) described this phenomenon as Conjunc- 
tion Reduction - '~whereby conjoined clauses 
that differ only in one item can be replaced by 
a simple clause that involves conjoining that 
item." In our example, the first and second 
propositions are 1-distinct at ARG2, and they 
are combined into a semantic structure repre- 
senting "A1 re-stocked coffee and tea in Aisle 
2 on Monday." If the third proposition is 1- 
distinct at ARG2 in respect to the result propo- 
sition also, the element "milk" in ARG2 of the 
third proposition would be similarly combined. 
In the example, it is not. As a result, we can- 
not combine the third proposition using only 
conjunction within a syntactic structure. 
When the next proposition and the result 
proposition have more than one distinct slot or 
their 1-distinct slot is different from the previ- 
ous 1-distinct slot, the two propositions are said 
to be multiple-distinct. Our approach in com- 
bining multiple-distinct propositions is different 
from previous linguistic analysis. Instead of re- 
moving recurring entities right away based on 
transformation or substitution, the current sys- 
tem generates every conjoined multiple-distinct 
proposition. During the generation process 
of each conjoined clause, the recurring ele- 
ments might be prevented from appearing at 
the surface level because the lexical chooser pre- 
vented the realization component from generat- 
ing any string for such redundant elements. Our 
multiple-distinct coordination produces what 
linguistics describes as ellipsis and gapping. 
Figure 4 shows the result combining two propo- 
sitions that will result in "A1 re-stocked tea on 
Monday and milk on Friday." Some readers 
might notice that PRED and ARG1 in both 
propositions are marked as RECURRING but 
only subsequent recurring elements are deleted 
at surface level. The reason will be explained in 
Section 5.4. 
5.3 Determine Sentence Boundary 
Unless combining the next proposition into 
the result proposition will exceed the pre- 
determined parameters for the complexity of a 
sentence, the algorithm wilt keep on combin- 
ing more propositions into the result proposi- 
tion using 1-distinct or multiple-distinct coor- 
dination. In normal cases, the predefined pa- 
rameter limits the number of propositions con- 
joined by multiple-distinct coordination to two. 
In special cases where the same slots across mul- 
tiple propositions are multiple-distinct, the pre- 
determined limit is ignored. By taking advan- 
tage of parallel structures, these propositions 
can be combined using multiple-distinct proce- 
dures without making the coordinate structure 
more difficult to understand. For example, the 
sentence "John took aspirin on Monday, peni- 
1223 
cillin on Tuesday, and Tylenol on Wednesday." 
is long but quite understandable. Similarly, 
conjoining a long list of 3-distinct propositions 
produces understandable sentences too: "John 
played tennis on Monday, drove to school on 
Tuesday, and won the lottery on Wednesday." 
These constraints allow CASPER to produce sen- 
tences that are complex and contain a lot of in- 
formation, but they are also reasonably easy to 
understand. 
5.4 Delete Redundant Elements 
Stage 4 handles ellipsis, one of the most dif- 
ficult phenomena to handle in syntax. In the 
previous stages, elements that occur more than 
once among the propositions are marked as RE- 
CURRING, but the actual deletion decisions 
have not been made because CASPER lacks the 
necessary information. The importance of the 
surface sequential order can be demonstrated 
by the following example. In the sentence "On 
Monday, A1 re-stocked coffee and \[on Monday,\] 
\[A1\] removed rotten milk.", the elements in 
MOD-TIME delete forward (i.e. the subsequent 
occurrence of the identical constituent disap- 
pears). When MOD-TIME elements are real- 
ized at the end of the clause, the same elements 
in MOD-TIME delete backward (i.e. the an- 
tecedent occurrence of the identical constituent 
disappears): "Al re-stocked coffee \[on Monday,\] 
and \[A1\] removed rotten milk on Monday." Our 
deletion algorithm is an extension to the Di- 
rectionality Constraint in (Tai, 1969), which 
is based on syntactic structure. Instead, our 
algorithm uses the sequential order of the re- 
curring element for making deletion decisions. 
In general, if a slot is realized at the front or 
medial of a clause, the recurring elements in 
that slot delete forward. In the first example, 
MOD-TIME is realized as the front adverbial 
while ARC1, "Ar', appears in the middle of the 
clause, so elements in both slots delete forward. 
On the other hand, if a slot is realized at the end 
position of a clause, the recurring elements in 
such slot delete backward, as the MOD-TIME 
in second example. The extended directionality 
constraint also applies to conjoined premodifiers 
and postmodifiers as well, as demonstrated by 
"in Aisle 3 and \[in Aisle\] 4", and "at 3 \[PM\] and 
\[at\] 9 PM". 
Using the algorithm just described, the result 
of the supermarket example is concise and eas- 
ily understandable: "A1 re-stocked coffee and 
1. The Base Plan called for one new fiber activa- 
tion at CSA 1061 in 1995 Q2. 
2. New 150mb_mux multiplexor placements were 
projected at CSA 1160 and 1335 in 1995 Q2. 
3. New 150mb.mux multiplexors were placed at 
CSA 1178 in 1995 Q4 and at CSA 1835 in 1997 
Q1. 
4. New 150mb_mux multiplexor placements were 
projected at CSA 1160, 1335 and 1338 and one 
new 200mb_mux multiplexor placement at CSA 
1913b in 1995 Q2. 
5. At CSA 2113, the Base Plan called for 32 
working-pair transfers in 1997 Q1 and four 
working-pair transfers in 1997 Q2 and Q3. 
Figure 5: Text generated by CASPER. 
tea in Aisle 2 and milk in Aisle 5 on Monday. 
A1 re-stocked bread in Aisle 3 on Friday." Fur- 
ther discourse processing will replace the second 
"Al" with a pronoun "he", and the adverbial 
"also" may be inserted too. 
CASPER has been used in an upgraded version 
of PLANDoc(McKeown et al., 1994), a robust, 
deployed system which generates reports for jus- 
tifying the cost to the management in telecom- 
munications domain. Some of the current out- 
put is shown in Figure 5. In the figure, "CSA" 
is a location; "QI" stands for first quarter; 
"multiplexor" and '~orking-pair transfer" are 
telecommunications equipment. The first ex- 
ample is a typical simple proposition in the do- 
main, which consists of PRED, ARC1, ARC2, 
MOD-PLACE, and MOD-TIME. The second 
example shows 1-distinct coordination at MOD- 
PLACE, where the second CSA been deleted. 
The third example demonstrates coordination 
of two propositions with multiple-distinct in 
MOD-PLACE and MOD-TIME. The fourth ex- 
ample shows multiple things: the ARC1 became 
plural in the first proposition because multi- 
ple placements occurred as indicated by sim- 
ple conjunction in MOD-PLACE; the gapping 
of the PRED '~ras projected" in the second 
clause was based on multiple-distinct coordina- 
tion. The last example demonstrates the dele- 
tion of MOD-PLACE in the second proposition 
because it is located at the front of the clause at 
surface level, so MOD-PLACE deletes forward. 
6 Linguistic Phenomenon 
In this section, we take examples from various 
linguistic literature (Quirk et al., 1985; van Oir- 
1224 
souw, 1987) and show how the algorithm devel- 
oped in Section 5 generates them. We also show 
how the algorithm can generate sentences with 
non-constituent coordination, which pose diffi- 
culties for most syntactic theories. 
Coordination involves elements of equal syn- 
tactic status. Linguists have categorized coor- 
dination into simple and complex. Simple coor- 
dination conjoins single clauses or clause con- 
stituents while complex coordination involves 
multiple constituents. For example, the coor- 
dinate construction in "John .finished his work 
and \[John\] went home." could be viewed as 
a single proposition containing two coordinate 
VPs. Based on our algorithm, the phenomenon 
would be classified as a multiple-distinct coordi- 
nation between two clauses with deleted ARG1, 
"John", in the second clause. In our algorithm, 
the 1-distinct procedure can generate many sim- 
ple coordinations, including coordinate verbs, 
nouns, adjectives, PPs, etc. With simple ex- 
tensions to the algorithm, clauses with relative 
clauses could be combined and coordinated too. 
Complex coordinations involving ellipsis and 
gapping are much more challenging. In 
multiple-distinct coordination, each conjoined 
clause is generated, but recurring elements 
among the propositions are deleted depending 
on the extended directionalityconstraints men- 
tioned in Subsection 5.4. It works because it 
takes advantage of the parallel structure at the 
surface level. 
Van Oirsouw (van Oirsouw, 1987), based on 
the literature on coordinate deletion, identified 
a number of rules which result in deletion under 
identity: Gapping, which deletes medial mate- 
rial; Right-Node-Raising (RNR), which deletes 
identical right most constituents in a syntactic 
tree; VP-deletion (VPD), which deletes iden- 
tical verbs and handles post-auxiliary deletion 
(Sag, 1976). Conjunction Reduction (CR), 
which deletes identical right-most or leftmost 
material. He pointed out that these four rules 
reduce the length of a coordination by delet- 
ing identical material, and they serve no other 
purpose. We will describe how our algorithm 
handles the examples van Oirsouw used in Fig- 
ure 6. 
The algorithm described in Section 5 can use 
the multiple-distinct procedure to handle all the 
cases except VPD. In the gapping example, the 
PRED deletes forward. In RNR, ARG2 deletes 
Gapping: John ate fish and Bill ¢ rice. 
P,_NR: John caught ¢, and Mary killed the ra- 
bid dog. 
VPD: John sleeps, and Peter does ¢, too. 
CRI: John gave ¢ ¢, and Peter sold a record 
to Sue. 
CR2: John gave a book to Mary and ¢ ¢ a 
record to Sue. 
Figure 6: Four coordination rules for identity 
deletion described by van Oirsouw. 
backward because it is positioned at the end of 
the clause. In CR1, even though the medial slot 
ARG2 should delete forward, it deletes back- 
ward because it is considered at the end position 
of a clause. In this case, once ARG3 (the BEN- 
EFICIARY "to Sue") deletes backward, ARG2 
is at the end position of a clause. This pro- 
cess does require more intelligent processing in 
the lexical chooser, but it is not difficult. In 
CR2, it is straight forward to delete forward be- 
cause both ARG1 and PRED are medial. The 
current algorithm does not address VPD. For 
such a sentence, the system would have gener- 
ated "John and Peter slept" using 1-distinct. 
Non-constituent coordination phenomena, 
the coordination of elements that are not of 
equal syntactic status, are challenging for syn- 
tactic theories. The following non-constituent 
coordination can be explained nicely with the 
multiple-distinct procedure. In the sentence, 
"The spy was in his forties, of average build, and 
spoke with a slightly foreign accent.", the coordi- 
nated constituents are VP, PP, and VP. Based 
on our analysis, the sentence could be gener- 
ated by combining the first two clauses using 
the 1-distinct procedure, and the third clause is 
combined using the multiple-distinct procedure, 
with ARG1 ("the spy") deleted forward. 
The spy was in his forties, \[the spy\] 
\[was\] of average build, and \[the spy\] 
spoke with a slightly foreign accent. 
7 Conclusion 
By separating the generation of coordination 
constructions into two tasks - identifying re- 
curring elements and deleting redundant ele- 
ments based on the extended directionality con- 
straints, we are able to handle many coordi- 
nation constructions correctly, including non- 
constituent coordinations. Through numerous 
1225 
examples, we have shown how our algorithm can 
generate complex coordinate constructions from 
clause-sized semantic representations. Both the 
representation and the algorithm have been im- 
plemented and used in two different text gener- 
ation systems (McKeown et al., 1994; McKeown 
et al., 1997). 
8 Acknowledgments 
This work is supported by DARPA Contract 
DAAL01-94-K-0119, the Columbia University 
Center for Advanced Technology in High Per- 
formance Computing and Communications in 
Healthcare (funded by the New York State 
Science and Technology Foundation) and NSF 
Grants GER-90-2406. 

References 
Charles B. Callaway and James C. Lester. 1997. 
Dynamically improving explanations: A revision- 
based approach to explanation generation. In 
Proc. of the 15th IJCAI, pages 952-958, Nagoya, 
Japan. 
Bob Carpenter. 1998. Distribution, collection and 
quantification: A type-logical account. To appear 
in Linguistics and Philosophy. 
Robert Dale. 1992. Generating Referring Expres- 
sions: Constructing Descriptions in a Domain of 
Objects and Processes. MIT Press, Cambridge, 
MA. 
Hercules Dalianis and Eduard Hovy. 1993. Aggre- 
gation in natural language generation. In Proc. of 
the ~th European Workshop on Natural Language 
Generation, Pisa, Italy. 
Michael Elhadad. 1993. Using argumentation to 
control lexical choice: A functional unification- 
based approach. Ph.D. thesis, Columbia Univer- 
sity. 
Lila R. Gleitman. 1965. Coordinating conjunctions 
in English. Language, 41:260-293. 
Michael A. K. Halliday. 1994. An Introduction to 
Functional Grammar. Edward Arnold, London, 
2nd edition. 
Xiaoron Huang and Armin Fiedler. 1996. Para- 
phrasing and aggregating argumentative text us- 
ing text structure. In Proc. of the 8th Interna- 
tional Natural Language Generation Workshop, 
pages 21-3, Sussex, UK. 
Ray Jackendoff. 1990. Semantic Structures. MIT 
Press, Cambridge, MA. 
Ronald M. Kaplan and Joan Bresnan. 1982. 
Lexical-functional grammar: A formal system for 
grammatical representation. In Joan Bresnan, ed- 
itor, The Mental Representation of Grammatical 
Relations, chapter 4. MIT Press. 
Martin Kay. 1984. Functional Unification Gram- 
mar: A formalism for machine translation. In 
Proc. of the IOth COLING and PPnd ACL, pages 
75-78. 
James D. McCawley. 1981. Everything that linguists 
have always wanted to know about logic (but were 
ashamed to ask). University of Chicago Press. 
Kathleen McKeown, Karen Kukich, and James 
Shaw. 1994. Practical issues in automatic doc- 
umentation generation. In Proe. of the 4th ACL 
Conference on Applied Natural Language Process- 
ing, pages 7-14, Stuttgart. 
Kathleen McKeown, Shimei Pan, James Shaw, 
Desmond Jordan, and Barry Allen. 1997. Lan- 
guage generation for multimedia healthcare brief- 
ings. In Proc. of the Fifth ACL Conf. on ANLP, 
pages 277-282. 
Anneke H. Neijt. 1979. Gapping: a eonstribution 
to Sentence Grammar. Dordrecht: Poris Publica- 
tions. 
Carl Pollard and Ivan Sag. 1994. Head- 
Driven Phrase Structure Grammar. University of 
Chicago Press, Chicago. 
Randolph Quirk, Sidney Greebaum, Geoffrey Leech, 
and Jan Svartvik. 1985. A Comprehensive Gram- 
mar of the English Language. Longman Publish- 
ers, London. 
Jacques Robin. 1995. Revision-Based Generation of 
Natural Language Summaries Providing Histori- 
cal Background. Ph.D. thesis, Columbia Univer- 
sity. 
John Robert Ross. 1967. Constraints on variables 
in syntax. Ph.D. thesis, MIT. 
Ivan A. Sag. 1976. Deletion and Logical Form. 
Ph.D. thesis, MIT. 
Donia R. Scott and Clarisse S. de Souza. 1990. Get- 
ting the message across in RST-based text gener- 
ation. In Robert Dale, Chris Mellish, and Michael 
Zock, editors, Current Research in Natural Lan- 
guage Generation, pages 47-73. Academic Press, 
New York. 
James Shaw. 1995. Conciseness through aggrega- 
tion in text generation. In Proc. of the 33rd A CL 
(Student Session), pages 329-331. 
James Shaw. 1998. Clause aggregation using lin- 
guistic knowledge. In Proc. of the 9th Interna- 
tional Workshop on Natural Language Genera- 
tion. 
Mark Steedman. 1990. Gapping as constituent coor- 
dination. Linguistics and Philosophy, 13:207-264. 
J. H.-Y. Tai. 1969. Coordination Reduction. Ph.D. 
thesis, Indiana University. 
Robert van Oirsouw. 1987. The Syntax of Coordi- 
nation. Croom Helm, Beckenham. 
Leo Wanner and Eduard Hovy. 1996. The Health- 
Doe sentence planner. In Proc. of the 8th Inter- 
national Natural Language Generation Workshop, 
pages 1-10, Sussex, UK. 
