CONVEYING IMPLICIT CONTENT IN NARRATIVE SUMMARW~S 
Malcolm E. Cook, Wendy G. Lehnert, David D. ~d 
Department of Computer and Information Science 
University of Massachusetts 
Amherst, Massachusetts 01003 
ABSTRACT 
One of the key characteristics of any summary is that it 
must be concise. To achieve this the content of the 
summary (1) must be focused on the key events, and (2) 
should leave out any information that she audience can 
infer on their own. We have recently begun a project on 
summarizing simple narrative stories. In our approach, we 
assume that the focus of the story has already been 
determined and is explicitly given in the story's lung-term 
representation; we concentrate instead on how one can plan 
what inferences an audience will be able to make when 
they read a summary. Our conduglon is that one should 
think about inferences as following from the audience's 
recognition of the central concepts in the story's plot, and 
then plan the textual structure of the ~mm~'y so as go 
reinforce that recognition. 
BACKGROUND 
This research builds on our previous work on narrative 
structure and generation. We are using Plot Units \[Lchnert 
1981\] to represent the structure of the ori~nal narrative, 
and use Mumble \[McDonald 1983\] to do the linguistic 
realiTAtion. To connect these two facilities we have a new 
interface and a new text plannin~ ©omponeng named 
Plot units are a technique for organizing the conceptual 
representation of a narrative in such a way that the 
topological structure of the representation directly indicates 
which events are central to the story and which are 
peripheral. A graph of connected plot units is constructed 
for a story as it is understood, based on the recognition of 
goal-oriented behavior by the characters and their affective 
reactions to events. Plot units summarize larger-scale 
relationships among explicit and implicit events in the story, 
and are oriented toward long term recall rather than 
appreciation of story style or specific wording. 
Mumble is a "realization" module for language 
generation; it takes a stream of output from a text planner 
and incrementally produces fluent, cohesive En~ligh text in 
accordance with the planner's spec/ficatious. The planner 
decides what information should be imparted and most of 
its rhetorical features; Mumble filters those decis/ons in 
accordance with grammatical constraints, handles syntax and 
morphology, and performs the "smoothing" operations that 
are required by the discourse context in which the 
information aIvears. 
1. This research was supported in part by the National 
Science Foundation under contracts IST-8217502 and 
IST-8104984, and in part by the Office of Naval Research 
under coatract N00014.E3-K-0~0. 
Precis stands between the plot unit graph and Mumble. 
h has been under development for a only short time and 
the ultimate form that its architecture will take is not yet 
fixed. We have so far been working bottom up, 
experimenting with different ways to combine the 
texts contributed by individual units and affect states, and 
trying to understand the consequences of the alternatives. 
We report here on one key "tactical" problem in narrative 
summarization which we refer to as conceptual ell~sis, 
omitting those events from a summary that we expect an 
audience to be able to infer on their own, and reinforcing 
that inference through a judicious choice of textual form. 
THE Nggn FOR CONCEPTUAL gLLlWb'IS 
Ever since the original work by Bartlett, researchers have 
appreciated that people who are remembering a story some 
time after they have heard it typically fail to distinguish 
between events that were explicitly stated in the stray and 
throe that they only inferred while reading it. Present day 
story understaDding systems act in a ~imilar way by 
malntainin~ Oilily a lingie conceptual record of what they 
have understood regardless of its murte \[Jcehi & 
Weischedel 1977, Graemer 1980, Dyer 1983\]. Since our 
summarization process starts from the conceptual 
representation of the story rather than the text itself, it too 
will be unable to make this distinction. 
This theory of memory has two consequences. One is 
that any decisions about what constituted the crux or point 
of the story must have been made at comprehension time 
rather than summarization time. This is one of the 
purposes of a plot unit ~tatiun. The other is that 
we now need to deliberately recalculate what information 
should be explicit in our summary and what should be left 
for the audience to infer; were this not done, the 
superfluous information in the summary would make it 
sound quite unnatural-as though it were being told by a 
person from a different society who did not have any 
commonsen~ understanding of the social context in which 
the story was set. How the explicit versus left-to-inference 
calculation turns out will vary with the tmmmary: the tame 
story can be summarized or retold in diffeie~.t ways 
depending on which character's point of view is taken or 
which events are emphasized. The plot unit graph is 
neutral on this question, and it will be an important part 
of what we do next in this research. 
Decisions about conceptual ellipsis are made prior to any 
of the linguistic decim'uns about form; they are however 
linked to those later decim'ons since some linguistic forms 
will be more effective than others in indicating to the 
audience that an inference is intended. Certain marked 
choices of form will suggest to the reader that particular 
implications were ~'m the mind of the writer" at the time 
of generation. The conceptual decm'ons are thus the source 
5 
of clependencies that must be carried forward to the point 
where the text-form decis/ens will be made in order that 
the i~ht re~liTntio nt are chOOSe~. By the lalne tokeD there 
will also be dependencies percolating back to the conceptual 
ellipsis decisions indicating what alternative realizations are 
actually available in a given case and thus whether a 
partienlar implication can be adequately supported by the 
information that is included and the way it is phrased. 
AN~ 
The followin8 simple stray will demomarate the gene_"al 
phenomenou. 
THE COMSYS STORY 
John and Mike were campot/ng for the same job at 
IBM. John got the job and Mike derided to stwn Ms 
own consulting f~m, COMSgS. W~hin three years, 
COMSg$ was flourfsMn&. By that time, John had 
become dissatisfied wfth IBM so he asked Mike for a 
job. M~te spU~d~y turned ~n down. 
A analysis of this text in terms of plot unJet has 
"Competition" as a central unit in the graph, which would 
make it a candidate bash for a snmmaEy of the story. All 
competition unim have this pattern: 
COMPETITION 
Agent1 Ageat2 
M1 
M2 
+ 
Underlying this levd of representation are the actual 
goals and events experieaced by the two charate~ In any 
competitim unit, we have: 
M1 : geal(agentl~xtll) 
M2 : goal(ageat2~2) 
+ : m_,y~_,goall,eventl) 
: failme(gml2~-veat2) 
with the additional constraints: 
Cl : event1 = evenl2 
C2 : goall and gml2 cannot boch be realized. 
(Note that in C1 the positive and negative acuudizatious 
are actually the mine event but from the point of view of 
two different charaeten.) 
In the COMSYS story the competition is between John 
and Mike over who will get a particular job at IBM. The 
instanfiatiou of the Competition unit in this story is: 
M1 : A-goall (John has-role #employee in M-job1 
(where ~employer = raM) 
M2 : A-goal2 (Mike hu-role ~-mployee in M-JOb1 
where ~employer = IBM) 
+ : m___o~__.A-goall , gm~IBMjohn)) 
- : fallm~A-guai2 , not($~re(WM,Mike))) 
where 
cl : eventl = event2 = hire(IBM,/ohn) 
¢2 : A-goall and A-goal2 cannot both be realized. 
At the time of this writing, Precis can specify any of 
the following texts for this instantiation of the Competition 
unit, prefeie~ces dilated by conceptual ellips~ aside. (Discourse 
fluency effects inch as verb phrase deletion or 
prouominalizatiou are put in by Mumble as it is realizing 
Pre~ ° wecification.) 
(a) "John wanted to work for IBM and so did Mike. They 
hired John and did not hire Mike." 
Co) "Both John and Mike wanted to work for IBM, but 
they hired John." 
(c) "Mike wanted to work for IBM, but they hired John. n 
These three choices vary according to how much of the 
content of the Competition unit they explicitly express. 
Choice A includes each of the four aHect states (MI, M2, 
+, .), smoothed somewhat by the recognition that MI and 
M2 share the same predicate. The very simplest choice.one 
that did not ¢apreu that commonality in its textual 
structure, e.g. "John wanted to work for IBM. Mike wanted 
to work for IBM. They Mred John and did not hire Mike.'-is 
cotnpletely nnnatural; people wouldn't say it. This minimal 
level of implicit information that the textual m'uctum must 
carry is ~.+dingiy not even made Prech" respom/bllity, but 
is in o*~d carried out automatically within Mumble. The 
alternative realization of this commonality, ruing a coujolned 
subject rather than verb phrase deletion, is taken to be a 
da:ba'ou and is not de.berated over by Pro:is. 
If we begin to include the constraints that accompany 
the Competltion Unit (Cl Slid C~) eXplicitiy in the tmmmagy 
then we can leave out mote of the affect states as 
in/erable. In choice B we make use of the first comgralnt, 
iJ~. that the pmitive and the negative acaualizations are 
consequences of the same event, to enable the omit'on of 
~ellg2, nog(hlgt~MlkeJ\]~M)), ~ the tegg of the lmmm~l~ 
by dropping the phrase "they did not ~re Mike". 
In our present vernon of choice B there is.no structural 
indicator of the constraint. It is probably no coincidena~. 
then that the text for B rounds a little odd-readers 
nnf~ with the orj~nal story wi\[! not really understand 
what the but is mppouxl to be communicating until they go 
further and make the deduction that there must oaly have 
been one job available. A better venion of B would 
probably be: "Both John and Mike wanted to work for IBM, 
bus they f~ly hired John", with the only acting as an explicit 
aruetural indicator of the information in the constraints. 
This addition can probably be licensed as a cotueque~e of 
the second constraint that only one of the two goals can be 
realized. At the time of this writing we do not yet have 
an adequately general mechanism for making 
observation and incorporating the on/y, so we have not 
included it among Precis" choices. 
It is intriguing that choice c, "Mike wanted to work for 
IBM, but they hired John", is probably the best of the three 
choices even though it requires the audience to do the most 
inferencing. In c we have omitted state Ml-that John 
wanted to work for IBM-yet the audience is able to 
recover this information quite easily given the presence of 
the but. Given the ease with which choice c is undemoud, 
we are led to the suggestion that there may be a very 
general "template" being recognized here-that choice c is 
seen by an audience as an instance of the pattern: 
<expression of agent A's goal>, 
but <.realization of agent B's goal> 
and that this template alwa~ carries with it the inference 
that the two goals must be incompatible and therefore A's 
goal has not be satisfied. 
Note that here again the choice would be improved by 
including an explicit lexical indication of the constraint: 
"Mike wanted to work for IBM, but they hired John ~nstead". 
We expect that most instances of these "rhetorical markers" 
in texts will turn out to be indicators of constraint-levcl 
information akin to our present cases, which raises the 
intriguing possibility that a general theory of how they are 
used might arise out of this kind of work in generation. 
SUMMARY 
Cuiie,,tly, we are working with two programs. PUGG 
(Plot Unit Graph Generator) operates on an affect-state 
representation of a story, and produces a graph or network 
of plot units that act as pointers to the o~e of the 
conceptual representation of the input story and organizes 
how it will be '~n~sented" to the program that plans the 
text of the summary, Precis. Precis is in the early stages 
of its development and so far can only use a single, core 
plot unit from the graph as the basis of the summ~'y of 
the story. 
Precis works at the interface between purely conceptuml 
and purely linguistic concideratiens as it makes its planning 
decisions. It chooses from a set of alternative specifications 
for the summary that vary according to which of the 
elements of the plot unit are included and which left to be 
inferred by the audience once they recognize the story as a 
case of competition. Precis can state the three alternative 
choices described above (and a few other sets like them), 
and Mumble can take those specifications and produce the 
indicated texts. However we do not as yet have any 
general mechanism for deciding which choice to prefer over 
the others. Perhaps such a decision mechanism will become 
apparent once these single unit summaries are embedded in 
a larger context, or possibly there is no reasonable basis for 
decision without more knowledge of the purpose of the 
summary or the ability of a particular audience to make 
these kinds of inferences (one might have to talk quite 
differently to young children for exam#e). In futu~ work 
we also hope to be able to work out a ~ basis for 
planning the use of infe~e~,ce-directing words like on/y or 
/nstead. 
REFERENCES 
Dyer, M. (1983) In~kTm Undermmd~: A Compu~r 
Model of Integrated Proesss~ for Narrative 
Comprehemien, Cambridge, Mass.: M1T Press. 
Graemer, A.fi c. (1981) Prose Comprehension Beyond the 
Word New York, N.Y.: Springer.Verlag. 
Joshi, A.K., and Woischedel (1977) Computation of a 
subclass of inferences: Presupposition and EntAilment, in 
Am J. of Comp. Lingulst~ 
Lehnert, W. (1982) Plot Units: A Narrative Summarization 
Strategy, in Lehnert, W. and Ringle, M. 0Eds.), Strategies 
for Natural Language Prmaush~, Hilisdale, NJ.: 
Lawrence Erlbeum Associates. 
Lehnert, W. (1983) "Narrative Complexity Based on 
Summarization Algorithms," ~ of the Seventh 
Internatlomd Joint Canf~ on Art/fkal ~, 
Karisruhe, Germany. 
McDonald, D. (1983) "National Language Generation as a 
Computatienal Problem - an Introduction" in Brady, M. 
and Berwick, R. (Eds.) Computatiomd Models of 
Discourse, Cambridge, Mass.: MIT Press. 
McDonald, D. (1982) "~)escription Directed Control: its 
Implications for Natural Language Generation", in 
Cercone (ed.) Computational IJn_maistics, Dublin: 
Pergamon Press. 
