Generating the Structure of Argument 
Chris Reed 
Department of IS and Computing 
Brunel University 
Middlesex, Uxbridge UB8 3PH, England 
Chris .Reed @ brunel, ac .uk 
Derek Long 
Department of Computer Science 
University of Durham 
South Road, Durham DH1 3LE, England 
D.P.Long@dur.ac.uk 
Abstract 
This paper demonstrates that generating 
arguments in natural language requires 
planning at an abstract level, and that the 
appropriate abstraction cannot be captured 
by approaches based solely upon coherence 
relations. An abstraction based planning 
system is presented which employs 
operators motivated by empirical study and 
rhetorical maxims. These operators include a 
subset of traditional deductive rules of 
inference, argumentation theoretic rules of 
refutation, and inductive reasoning patterns. 
The paper presents a unified system in 
which the various argument forms are 
employed in generating rich, complex 
structures for persuasive text. 
Introduction 
The ability to generate arguments in natural 
language is attracting wide-ranging research 
interest, and it is becoming clear that the 
problem is also stimulating investigation of a 
number of problems of importance to natural 
language generation (NLG) as a whole (Reed 
and Long, 1997a). Argumentation is particularly 
appropriate as an NLG problem both because it 
is more highly structured than other forms of 
natural language, and because there are a variety 
of established metrics developed in rhetoric and 
social psychology for judging the resultant 
quality of a text. These advantages are being 
exploited in the design and implementation of 
the ~hetor/ca system, of which the current work 
forms a part. 
1 Problems with RST 
A number of limitations of the generative 
capacity of Rhetorical Structure Theory (RST) 
(Mann and Thompson, 1988) have recently been 
identified - most notably, its inability to 
adequately handle intention (Moore and Paris, 
1994). Through investigation of a particular 
genre - persuasive text - it has become clear that 
RST suffers from a much wider catalogue of 
crippling restrictions, severely limiting its 
applicability to generation in this genre and 
questioning its suitability elsewhere. 
Mann and Thompson discuss the key 
role played by the notion of nuclearity - that 
relations hold between one nucleus and one 
satellite. They do, however, concede (p269) that 
there are a few cases in which nuclearity breaks 
down - and these they regard as rather unusual. 
The two types of multi-nuclear constructs they 
identify are enveloping structures -"texts with 
conventional openings and closings" and 
parallel structures - "texts in which parallelism 
is the dominant organising pattern". Both of 
these are not just common in argument, but form 
key components. Enveloping structures are 
precisely what are described by, for example, 
Blair (1838) (citing Cicero), when presenting the 
dissection of argument into introduction, 
proposition, division, narration, argumentative, 
pathetic and conclusion (these are by no means 
obligatory in every argument, nor is there any 
great consensus over this particular 
characterisation; most authors, however, would 
agree that some such gross structure, usually 
involving introduction and conclusion, is 
appropriate). These structures are found with 
great frequency in natural argument, and cannot, 
therefore, be ignored. Parallel structures form 
the very basis of argument, since only the most 
trivial will involve lines of reasoning in which a 
single premise supports a single conclusion. 
Multiple subarguments conjoined to support a 
1091 
conclusion are the norm (see for example, 
Cohen (1987), Reed and Long, 1997b), and 
these necessarily form parallel structures. 
Another shortcoming is highlighted by a 
dissonance between RST and argument analysis 
(see Eemeren et al. (1996) for a review). A 
given text may be amenable to multiple RST 
analyses - not just as a result of ambiguity, but 
because there are, at a fundamental level, 
"multiple compatible analyses". This contrasts 
with the view in argumentation theory, where 
one argument has a single, unequivocal 
structure. There may, of course, be practical 
problems in identifying this structure, and two 
analysts may disagree on the most appropriate 
analysis (and indeed this latter has a close 
parallel in RST, since different analysts are at 
liberty to make different 'plausibility 
judgements' as to the aims of the speaker). The 
presence of these problems, however, is not 
equivalent to claiming that arguments may 
simply have more than one structure, a claim 
which would pose insurmountable problems to 
the evaluation process (- argumentation theory 
aims to determine a means of classifying an 
argument as either good or bad, and the presence 
of inherent structural multiplicity would present 
the possibility of an argument being 
simultaneously good and bad). 
Finally, there is a more intuitive problem 
with RST, highlighted by analysing argument 
structure. Although there is much debate over 
the number and range of rhetorical relations (e.g. 
Hovy, 1993) there seems to be no way of 
dealing with the idea of argumentative support. 
In the first place, as Snoeck-Henkemans (1997) 
points out, Motivation, Evidence, Justification, 
Cause, Solutionhood and other relations could 
all be used argumentatively (as well, of course, 
as being applicable in non-argumentative 
situations). Elhadad (1992) draws a similar 
conclusion (though his list of potentially 
argumentative relations is somewhat shorter). 
Thus it is impossible to identify an 
argumentative relation on the basis of RST 
alone. Secondly, RST offers no way of capturing 
higher level organisational units, such as Modus 
Ponens, Modus Tollens, and so on. For although 
their structure (or at least the structure of any 
one instance) can be represented in RST - and, 
given Marcu's (1996) elegant extensions, even 
their hierarchical use in larger units - adopting 
this approach necessitates a lower level view. It 
becomes impossible to represent and employ a 
Modus Tollens subargument supporting the 
antecedent of a Modus Ponens - rather, the 
situation can only be characterised as P 
supporting through one of the potentially 
argumentative RST relations Q, and showing 
that -Q, so -P, and -P then supporting through 
one of the potentially argumentative RST 
relations R, therefore R. Apart from being 
obviously cumbersome, the representation has 
lost the abstract structure of the argument 
altogether, and is not generalisable and 
comparable to other similar argument structures. 
(It could perhaps be maintained that such 
structures could be represented as RST schemas, 
but there are several problems with such an 
approach: in the first place, schemas cannot 
abstract from individual relations, so there 
would need to be a separate 'Modus Ponens' 
schema for each possible argumentative support 
relation; furthermore, the optionality and 
repetition rules of schema application (Mann 
and Thomson, 1988, p248) are not suited to 
argument, as they license the creation of 
incoherent argument structure). 
It is for these reasons, and particularly, 
the last, that although RST plays an important 
role in the current work, it is subsumed by a 
layer which explicitly represents argumentative 
constructs. These constructs can be mapped on 
to the most appropriate set of RST relations 
(thus, for example, the implicature in an MP 
may be realised into any one of the potentially 
argumentative relations mentioned above). The 
approach thus maintains the generative 
capabilities of RST (particularly when extended 
along the lines of Marcu (1996) to ensure 
coherency through adducement of canonical 
ordering constraints), whilst embracing the 
intuitive argumentative relationships at a more 
abstract level. It is these latter relationships 
which characterise the structure of the argument 
(i.e. the structure which argumentation theory 
strives to determine). The relationships are also 
unambiguous: a single argument has exactly one 
structure at this level abstraction (though 
multiplicity is not thereby prevented at the RST 
level). Further, parallelism occurs only at the 
higher level of abstraction (multiple 
1092 
subarguments contribute to a conclusion, but 
each subargument is mononucleaic), and 
similarly, enveloping structures are also 
characterised only at the higher level (thus the 
RST is restricted to a predominantly 
mononucleaic structure). Finally, complete 
argument texts are not obliged to have complete 
RST trees. For although most parts of a text are 
likely to have unifying RST analyses, and 
although there must be a single overarching 
structure at the highest level of abstraction, the 
refinement to RST need not enforce the 
introduction of rhetorical relations between 
parts. This expands the flexibility and generative 
capacity of the system encompassing a greater 
proportion of coherent arguments (including, for 
example, those found in laws and contracts). 
2 Abstraction-Based Planning 
The structure of argument is thus planned at a 
level more abstract than RST. To exploit the 
intrinsic hierarchical structuring of argument, 
the current work makes use of AbNLP (Fox and 
Long, 1995), a hierarchical planner based upon 
the concept of encapsulation, whereby the body 
of an abstract operator contains goals rather than 
operators, and further, that the body of an 
operator is not opened up until an entire abstract 
plan has been completed (i.e. there are no goals 
left unfulfilled at that level of abstraction). On 
completion of an abstract plan (which can be 
seen, in discourse planning, as a skeletal outline 
of what is to be communicated), the refinement 
operation opens up all the abstract operator 
bodies, such that the structure and constraints 
determined at one level of abstraction are 
propagated to the next level down. As a 
consequence, many choices which might have 
been considered during planning of an argument 
at the detailed level can be pruned as they 
become inconsistent with the abstract plan. Such 
an approach has the potential to considerably 
improve upon the performance of a classical 
planner, (Bacchus and Yang, 1992). The use of 
AbNLP in a framework for argumentative 
discourse planning is discussed in more detail in 
Reed et al. (1996). 
The operators employed by AbNLP 
utilise a highly parsimonious set of intentional 
goals. Belief goals are used to build the content 
of an argument (as in much other NLG work); 
saliency goals to express the intention to convey 
information to the hearer (following a notion of 
saliency similar to that proposed in Walker, 
1996); and topic manipulation goals to control 
the focus of attention through the discourse. The 
roles of these goals and their interrelationships 
are explored in relation to the information- 
intention-attention model of Grosz and Sidner 
(1986) in more detail in Reed and Long (1997a). 
3 Deductive Operators 
The choice of operators implemented in 
the Rhetorica system has been influenced by a 
number of factors. The rules of inference are 
clear candidates for operationalisation: moves 
such as Modus Ponens are clearly vital 
components of any argument - though, as noted 
in Grosz and Sidner (1986), p201, it is 
inappropriate to view the implication step as one 
of conventional material implication. The 
relationship is rather one of support - the hearer 
must be brought to believe that (given the 
current context and domain of discourse) the 
first proposition warrants, in part, concluding the 
second. Even given this weaker, predicate-based 
reading of a Modus Ponens argument, it is still 
unclear that any of the other rules of inference 
(which are, after all, formally redundant) should 
be necessary. The answer lies in the second 
consideration, which is entirely empirical - the 
reason that the argument planning needs to be 
able to employ other rules of inference is that 
such argument forms occur naturally. Modus 
Tollens, for example, is perfectly common, with 
numerous (real world) examples in 
argumentation texts such as Fisher (1988). 
Further, there is a variety of evidence which 
suggests that Modus Tollens in fact occupies a 
crucial position in human reasoning (Ohlsson 
and Robin (1994) cite examples not only from 
psychology, artificial intelligence and empirical 
observation, but also by reference to classic 
examples of Euclid, Galileo, etc.) 
Disjunctive Syllogisms are also found 
reasonably often, but the remaining rules of 
inference are found very rarely. For this reason, 
only the three logical argument forms, MP, MT 
and DS, are currently implemented. 
The three deductive operators are shown 
1093 
MP (Ag, X, P) 
Shell: Precond: 
Add: 
Body: Goals: 
MT (Ag, X, P) 
Shell: Precond: 
Add: 
Goals: Body: 
DS (Ag, X, P) 
Shell: 
Body: 
X, (X -~ P) 
BEL (Ag, ~P) 
BEL (Ag, P) 
t0:PUSH TOPIC(arg(X,P)) 
tI:BEL(Ag, X) 
t2:IS_SALIENT(Ag,X,arg(X,P)) 
t3:BEL(Ag, X-)P) 
t4:IS SALIENT(Ag,X-~P,arg(X,P)) 
t5:POP_TOPIC(arg(X,P)) 
X, (-P -~ X) 
BEL (Ag, ~P) 
BEL (Ag, P) 
t0:PUSH_TOPIC(arg(-X,P)) 
tI:BEL(Ag,~X) 
t2:IS_SALIENT(Ag,-X,arg(-X,P)) 
t3:BEL(Ag,~P-~X) 
t4:IS_SALIENT(Ag,-P-+X,arg (~X, P)) 
t5:POP_TOPIC(arg(-X,P)) 
Precond: X, (X v P) 
BEL (Ag, ~P) 
Add: BEL (Ag, P) 
Goals: t0:PUSH_TOPIC(arg(X,P)) 
tl :BEL (Ag, ~X) 
t2:IS SALIENT(Ag,~X,arg(X,P)) 
t3:BEL(Ag, X v P) 
t4:IS SALIENT(Ag,X v P,arg(X,P) ) 
t5 : POP_TOPIC (arg (X, P) ) 
Figure 1. The deductive operators 
in Figure 1 (the orderings constraining the body 
goals - enforcing initial and terminal positions 
for PUSH_TOPIC and POP_TOPIC respectively - 
are omitted for clarity). The preconditions on 
each operator act as filters on their applicability 
(so that, for example, the version of MP shown 
is only applicable for the situation in which the 
hearer believes the negation of the conclusion). 
The body is bounded by the topic manipulators 
which take as a parameter the topic for the 
current argument, of the form arg(X, P), a 
generic expression representing an argument 
concluding P using a premise X (used to abstract 
from Modus Ponens, Modus Tollens, etc.). The 
saliency goals also employ the same context 
parameter: this is used at a later stage to ensure a 
basic level of coherency (by placing utterances 
within the appropriate focus space) - discussion 
of this mechanism is beyond the scope of this 
paper. Finally, the operator bodies also include 
goals of belief which are satisfied (after 
refinement) by further applications of the 
operators (e.g. the goal tl in a given MP could 
be fulfilled by an MT) 
4 Refutation operators 
In addition to these deductive operators, 
5~hetorica also employs pseudo-deductive 
operators, by means of which counter- 
counterargumentation structures can be 
developed. The importance of including such 
refutation in an argument has been conclusively 
demonstrated in social psychology (Hovland, 
1957). The operators required to effect the 
generation of such structure are closely related 
to the notions of conflict explored by Haggith 
(1996), and draw upon the distinction between 
rebutting and undercutting counterarguments, 
identified in (Toulmin, 1958). Given the 
situation portrayed in Figure 2, in which the 
speaker believes p because of a, and also 
disbelieves b because of d and e, and the hearer 
believes -p supported by b and c, a number of 
options are available to the speaker. The 
conventional MP operator discussed above can 
be employed to support p by a - this is rebuttal. 
In addition, the hearer's belief in -p can be 
undercut by arguing against one of its supports, 
namely, b. 
-b /\ 
d e a 
H -p /\ 
b c 
Figure 2. Sample scenario 
There are thus no new operators for 
rebutting, since those in Figure 1 already fulfil 
that role. Undercutting, however, requires two 
new operators, one which characterises a 
refutation of a premise (UCP), and one which 
characterises a refutation of the validity of an 
inference (UCI). The operator definitions are 
shown below in Figure 3. 
There are several points to note about 
these definitions. First, that they are fairly loose, 
since the speaker is not obliged to believe the 
falsity of the hearer's premise, merely be able to 
persuade the hearer of that falsity (though the 
speaker is somewhat constrained by rules of 
terminal goal fulfilment - in particular, the 
BEE (H, P) goal is prohibited from fulfilment by 
1094 
UCP (Ag, X, P) 
Shell: Precond: 
Add: 
Body: Goals: tO 
tl 
t2 
t3 
t4 
t5 
P, BEL(Ag, -P) 
BEL(Ag, X), BEL(Ag, X-gP) 
BEL(Ag, P) 
BEL(Ag, -X) 
:PUSH_TOPIC(arg(-X,P)) 
:IS_SALIENT(Ag,X,arg(-X,P)) 
:BEL(Ag,-X) 
:IS_SALIENT(Ag,-X,arg(-X,P)) 
:IS_SALIENT(Ag,X-~P,arg(-X,P)) 
:POP_TOPIC(arg(-X,P)) 
UCI (Ag, X, P) 
Shell: Precond: -(X-+P), BEL(Ag, -P) 
BEL(Ag, X), BEL(Ag, X-~P) 
Add: BEL(Ag, P) 
BEL(Ag, -(X-~P)) 
Body: Goals: t0:PUSH TOPIC(arg(-(X-+P),P)) 
tI:IS_SALIENT(Ag,X, 
arg(-(X-~P),P)) 
tI:BEL(Ag,-(X-~P)) 
t2:IS_SALIENT(Ag,-(X-~P), 
arg(-(X-~P),P)) 
t5:POP_TOPIC(arg(-(X-eP),P)) 
Figure 3. Refutation operators 
any means other than substantial support). 
Secondly, in the UCP operator, it is necessary to 
make sure that the hearer is aware that the 
premise supports his conclusion - but clearly, 
the speaker doesn't want to offer any further 
support for the inference, hence the absence of a 
belief goal corresponding to IS_SALIENT (Ag, X 
V, arg (-X, V)). Lastly, a similar issue faces 
the UCI operator - the tl goal expresses the 
need to make the premise salient before 
attacking it. Indeed, stating counterarguments is 
the key to counter-counterargumentation: just 
mentioning a counterargument can bolster a 
claim. The goal is particularly interesting both 
from a realisation point of view (where 
information can be exploited that x is being 
made salient in the context of an argument from 
~x), and an ordering point of view (whether or 
not statement should precede refutation, and 
then whether or not UCP/I argumentation should 
precede pro support is a major issue of debate in 
psychology, Hass and Linder, 1972). 
The deductive operators, however, do 
not offer the full range of argument forms found 
in natural text. One major omission is the class 
of inductive operators, including analogy, 
inductive generalisation, and causal relation. The 
framework is designed to admit all these 
operators, but the current work concentrates 
upon inductive generalisation. 
Inductive generalisation (IG) is of 
particular interest for a number of reasons. The 
first is the frequency with which various naively 
statistical and probabilistic arguments are 
employed in natural language. More 
importantly, though, are the problems faced in 
argumentation theoretic analyses of inductive 
generalisation. Freeman (1991) examines the 
problems in depth, and, building on Toulmin's 
work (1958), and its criticisms, comes to a well 
justified conclusion that IG should be treated as 
a convergent arrangement. His argument rests 
largely on the distinction between the 'ground 
adequacy' and 'relevance' questions: in 
analysing any argument as dialogical, the analyst 
can look at any two premises and infer that some 
imaginary opponent had asked a question after 
the first premise to elicit the second. If that 
question was 'Can you give me another reason?' 
(ground adequacy), the resulting structure is 
convergent, whereas if that question was 'Why 
does the premise support the conclusion?' 
(relevance), the resulting structure is linked. An 
inductive generalisation is thus based on a 
number of premises between which an 
imaginary opponent continually asks the ground 
adequacy question. The reason, Freeman claims, 
that inductive generalisation may be intuitively 
mistaken for a linked structure is that each 
premise in itself lends only very weak support to 
the conclusion, and that this generally results in 
assumption of linkage. Freeman's work and its 
relation to other accounts of linked and 
convergent argumentation is explored more fully 
in (Reed and Long, 1997b). 
In following Freeman's attractive 
account of IG, it may appear that the required 
convergent structure can be fully accounted for 
in the existing framework, by allowing the 
standard iterative fulfilment of goals of belief 
discussed (Reed and Long, 1997a). However, 
Freeman's account, because it is analytic, omits 
the rather obvious fact that premises in an IG 
have something in common with each other and 
with the conclusion. That a premise in an IG is 
related to the conclusion in some respect cannot 
be handled simply by iterating through all 
available supports for an argument, since there is 
no way to select all and only those premises 
which support the conclusion in the given 
respect. Furthermore, it is important that the IG 
itself is seen as a unit, since it is quite 
1095 
inappropriate for subsequent ordering heuristics 
to be at liberty to intersperse various deductive 
premises for a conclusion in the midst of the 
inductive premises (or further, that if there exist 
two or more IGs supporting the same conclusion 
- each employing a different common attribute - 
it is inappropriate to mix premises from the 
various arguments). Seeing the whole IG as a 
unit enables appropriate scoping for reordering: 
the premises within the unit can be reordered 
wholly within the unit, and the unit itself can be 
moved around wholesale with respect to the 
other premises. An IG is thus viewed in the 
current work as a premise. This is illustrated in 
the diagrammatic argument notation as a 
phantom node, as shown in Figure 4. 
/ 
:..... -..... 
" IG) 
al a2 ...... a n 
P 
X 
b c 
<~- phantom node 
Figure 4. Inductive Generalisation 
Thus the IG premise phantom node is generated 
along with all the other premises for a given 
conclusion. Then, after refinement, the 
individual premises within the inductive 
argument are determined, occurring concurrently 
with identification of supports for the other 
premises which are at the level of the inductive 
generalisation. In the scenario illustrated in 
Figure 4, for example, the first round of 
planning identifies that there are three supports 
for the conclusion p, namely, a Modus Ponens 
argument from each of b and c, and an inductive 
generalisation. After an appropriate order is 
determined for these three, refinement opens up 
the bodies of the operators, and the supports for 
b and c are identified, and the inductive 
generalisation is fleshed out to include a~ 
through a. The process of building an inductive 
generalisation thus involves two different 
operators: the IG operator, which identifies that 
an inductive generalisation is appropriate, and 
the ISUP operator, which is used to select each 
inductive premise. To prevent an inductive 
generalisation from being considered at every 
turn, the precondition list on IG states that there 
must exist at least one premise which can be 
used inductively - this is a bare minimum since 
an inductive generalisation employing a single 
premise is clearly very weak. Strengthening the 
notion of inductive generalisation is a trivial task 
of increasing the minimum number of premises 
which must exist for the application of IG to be 
licensed. 
The complete definitions for IG and 
ISUP are given below in Figure 5. 
IG (Ag, 
Shell: 
Body: 
P, R) 
Precond: HAS_PROPERTY(P,R) 
HAS PROPERTY(X,R) 
Add: BEL(Ag, P) 
Goals: t0:PUSH_TOPIC(arg(R,P)) 
t2:BEL(Ag, IG(R,P)) 
t3:IS_SALIENT(Ag, IG(R,P), 
arg(R,P)) 
t4:POP_TOPIC(arg(R,P)) 
ISUP (Ag, P, R) 
Shell: Precond: 
Add: 
Body: Goals: 
HAS_PROPERTY (X, R) 
BEL(Ag, IG(R,P)) 
t0:PUSH TOPIC (HAS_PROPERTY(X,R)) 
tl : BEL (Ag, X) 
t2 : IS_SALIENT (Ag, X, 
HAS_PROPERTY (X, R) ) 
t3:BEL(Ag,HAS PROPERTY(X,R)) 
t4 : IS_SALIENT (Ag, 
HAS PROPERTY(X,R), HAS_PROPERTY(X,R)) 
t5:POP_TOPIC(HAS PROPERTY(X,R) 
Figure 5. Refutation operators 
A single new function is required to express the 
common feature of the premises and conclusion 
which license the inductive generalisation - this 
is implemented as a simple function call to 
r~S_PROPERTY which determines whether or not 
a given property holds for a given proposition 
(this functionality is encapsulated in an 'oracle' 
following \[Cohen87\]). In both IG and ISUP, the 
notion of 'support' is thus eschewed altogether 
and simply remains implicit in the fact that 
propositions are the same in respect R. It is not 
necessary to introduce a new notion of support. 
Conclusion 
This paper has presented a number of features of 
the ~(hetorica system, and has introduced the 
deductive, refutation and inductive 
generalisation operators which are employed to 
generate the abstract structure of an argument. In 
1096 
related work, this abstract structure is often lost 
- certainly in coherence relation based NLG 
(such as operational RST), but also in (Elhadad, 
1992) (which captures some, but not all of the 
commonly found argument structures) and in 
(Maybury, 1993) (which fails to capture the 
hierarchical nature of argument). 
Evaluation of non-task-oriented NLG is 
difficult, particularly when the output is not text, 
but a plan of primitive operators. However, 
several evaluative observations support the 
approach. First, though only touched upon here, 
the planning process produces a partially 
specified plan in which the underspecification is 
precisely that licensed by Cohen-like constraints 
on argument coherency (Reed and Long, 1997a) 
appropriated from empirical studies in 
argumentation theory. Furthermore, the 
approach enables these coherency constraints to 
be expressed in a tractable way. Finally, a 
comparison of system output with natural 
arguments (of equivalent propositional content) 
in a small corpus suggests that the constraints of 
coherency discussed here do indeed ensure the 
generation of coherent argument structures, and 
that the interplay between them and constraints 
of persuasive effect facilitate the construction of 
natural language arguments which are both 
coherent and effective. 

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