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<Paper uid="W97-0714">
  <Title>ized with Macromed\]a D~rector CommumcaUon between CLOS and Macromecha Director Is medmted by Apple Events</Title>
  <Section position="3" start_page="0" end_page="89" type="intro">
    <SectionTitle>
1 Introduction
</SectionTitle>
    <Paragraph position="0"> The SlmSum (Slmulatmn of Summarizing) system does what its name pronuses It simulates summanzang of human experts and thus produces a computaUonal cogmUve model of their processing The model concentrates on the specific features of summanzmg It presupposes &amp;quot;normal&amp;quot; text understan&amp;ng and text producUon The slmulaUon serves scientific and presentational purposes * As usual, the computer model serves to explain and check the empirical cogmtlve model winch ~s Rs foundation * It prepares a cogmtlvely grounded approach to automatic summarnzmg, something hke agents runmng through the net and m response to a user's query, bnngmg home a reasonably short statement (a summary) of the knowledge avmlable * To its users of today, SlmSum shows m a movle-hke style how expert summanzers perform real-world workJng processes, thus complementing a textbook about summanzmg The advantage of the simulation ressembles that of a flight simulator As pflotes steer through possibly difficult situations ~n the physical world, summarizers work their way through a flood of mforrnatlon Both activities are cogmt~vely demanchng People understand them better if they are presented wRh them m reahsuc setungs S~mulatmn approaches to summarizing are few and far between, but one can point to the SUSY system (Fum et al, 1982, 1984 and 1985) as an ancestor of S~mSum SUSY aimed at following human performance m a hn~ted way, though keeping at a distance from real s~mulauon S~mSum represents progress wnh respect to SUSY, because ~t ts empmcally founded, ~t does a real slmulauon, and ~t ~s implemented Furthermore, S~mSum renovates through ~ts mulUmed~a user interface For practical reasons, the SlmSum simulation ts restricted to 20 working steps involving 79 agents They were chosen from an empmcal cogmUve model (a &amp;quot;grounded theory&amp;quot; Glaser &amp; Strauss, 1980, see also Lincoln &amp; Guba, 1985, for the way to lmplementatton refer to Schrelber et al 1993) of summarizing wlnch comprises an intellectual toolbox of 552 strategies,, knowledge about the process organization and a set of interpreted. summarizing steps Its basis are 54 summanzmg processes of 6 experts from the USA and Germany The summarization processes were recorded by tinnlang-aloud protocols (Ericsson &amp; Simon 1980, 1984) and analyzed under the sclentfflc umbrella of the discourse comprehension model proposed by van DIjk and Kmtsch (1983) The experts being professmnals worlang m the context of mformatmn systems, three forms of summa- null nzmg occur abstractmg, mdexmg and classifying null A simulation system such as SlmSum is bound to empmcal vah&amp;ty, giving a reverse engmeenng of a cognmve process Such a reconstruction of human cognmve actavmes is possible because human experts subdlwde long cogmuve efforts hke sumrnanzmg into modules, called here working steps In the thinking-aloud record they are separated by boundary signals such as pauses or mterjec-Uons It Is these working steps that are reconstructed Put m sequence, they yield the model of the process Since the sequences m the SlmSum system are short, there ~s almost no chance for for seriously dealing w~th metacogmtlon (Flavell 1981) m the system Hence metacognmve knowledge ts simply hard-coded m the form of working plans etc.</Paragraph>
    <Paragraph position="1"> In the following, SlmSum ts explmned first at the macro level of system archRecture and system components Then the descnptlon narrows down to the xmcro level of processmg After a demonstration of the text representation, two exemplary relevance agents are discussed</Paragraph>
  </Section>
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