  Concept-to-text generation typically employs a pipeline architecture, which
often leads to suboptimal texts. Content selection, for example, may greedily
select the most important facts, which may require, however, too many words to
express, and this may be undesirable when space is limited or expensive.
Selecting other facts, possibly only slightly less important, may allow the
lexicalization stage to use much fewer words, or to report more facts in the
same space. Decisions made during content selection and lexicalization may also
lead to more or fewer sentence aggregation opportunities, affecting the length
and readability of the resulting texts. Building upon on a publicly available
state of the art natural language generator for Semantic Web ontologies, this
article presents an Integer Linear Programming model that, unlike pipeline
architectures, jointly considers choices available in content selection,
lexicalization, and sentence aggregation to avoid greedy local decisions and
produce more compact texts, i.e., texts that report more facts per word.
Compact texts are desirable, for example, when generating advertisements to be
included in Web search results, or when summarizing structured information in
limited space. An extended version of the proposed model also considers a
limited form of referring expression generation and avoids redundant sentences.
An approximation of the two models can be used when longer texts need to be
generated. Experiments with three ontologies confirm that the proposed models
lead to more compact texts, compared to pipeline systems, with no deterioration
or with improvements in the perceived quality of the generated texts.
