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<Paper uid="W00-1417">
  <Title>OLAP context Dimensions</Title>
  <Section position="4" start_page="129" end_page="130" type="relat">
    <SectionTitle>
3 Related work in content aggregation
</SectionTitle>
    <Paragraph position="0"> The main previous works on content aggregation are due to: o (Dalianis 1995, 1996), whose ASTROGEN system generates natural language paraphrases of formal software specification for validation purposes; (Huang and Fiedler 1997), whose PROVERB system generates natural language mathematical proofs from a theorem prover reasoning trace; (Robin and McKeown, 1996), whose STREAK system generates basketball game summaries from a semantic network  representing the key game statistics and their historical context; (Shaw 1998), whose CASPER discourse and sentence planner has been used both in the PLANDoc system that generates telecommunication equipment installation plan documentation from an expert system trace and the MAGIC system that generates extracted from a dimensional data warehouse hypercube. In contrast, the other systems all take as input either a semantic network extracted from a knowledge base or a pre-linguistic representation of the text to generate such as Meteer's text structure (Meteer 1992) or Jackendoffs semantic structure (Jackendoff 1985). Such natural language processing ICU measurements.</Paragraph>
    <Paragraph position="1"> In this section, we briefly compare these research efforts with ours along four dimensions: (1) the definition of aggregation and the scope of the aggregation task implemented in the generator, (2) the type of representation the generator takes as input and the type of output text that it produces, (3) the generator's architecture and the localization of the aggregation task within it, and (4) the data structures and algorithms used to implement aggregation.</Paragraph>
    <Section position="1" start_page="130" end_page="130" type="sub_section">
      <SectionTitle>
3.1 Definition of the aggregation task
</SectionTitle>
      <Paragraph position="0"> The definition of aggregation that we gave at the beginning of previous section is similar to those provided by Dalianis and Huang, although it focuses on common feature factorization to insure aggregation remains a proper subset of sentence planning. By viewing aggregation only as a process of combining clauses, Shaw's definition is more restrictive. In our view, aggregation is best handled prior to commit to specific syntactic categories and the same abstract process, such the algorithm of Fig. 10, can be used to aggregate content units inside linguistic constituents of any syntactic category (clause, nominal, prepositional phrases, adjectival phrases, etc.). In terms of aggregation task coverage, HYSSOP focuses on paratactic forms of aggregation. In contrast, ASTROGEN,</Paragraph>
    </Section>
    <Section position="2" start_page="130" end_page="130" type="sub_section">
      <SectionTitle>
3.2 Input
</SectionTitle>
      <Paragraph position="0"> output text patient status :.~.:br~efs .~r~in~ .: m6di ~a~. ~ .~` :.~6iiented~:inputk:tend `t~ ~ gi.mp~ify~..the: ~vera~ .text representation and generated A second characteristic that sets HYSSOP apart from other generators perfornfing aggregation is the nature of its input: a set of data cells generation task and hide important issues that come up in real life applications for which raw data is often the only available input. In terms of output, HYSSOP differs from most other systems in that it generates hypertext instead of linear text. It thus tackles the content aggregation problem in a particularly demanding application requiring the generator to simultaneously start from raw data, produce hypertext output and enforce conciseness constraints.</Paragraph>
    </Section>
    <Section position="3" start_page="130" end_page="130" type="sub_section">
      <SectionTitle>
3.3 Generation architecture and
</SectionTitle>
      <Paragraph position="0"> aggregation localization While its overall architecture is a conventional pipeline, HYSSOP is unique in encapsulating all aggregation processing in the sentence planner and carrying it out entirely on a deep semantic representation. In contrast, most other systems distribute aggregation over several processing components and across several levels of internal representations: deep semantic, thematic and even surface syntactic for some of them.</Paragraph>
    </Section>
    <Section position="4" start_page="130" end_page="130" type="sub_section">
      <SectionTitle>
3.4 Data structures and algorithms for
</SectionTitle>
      <Paragraph position="0"> aggregation All previous approaches to aggregations relied on rules that included some domain-specific semantic or lexical information. In contrast, the aggregation algorithm used by HYSSOP is domain independent since it relies only on (1) generic matrix row and column shuffling operations, and (2) on a generic similarity . =:meas ure.betveeen-arbi trary data cells.</Paragraph>
    </Section>
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