Question: What model do the authors propose in the paper?
Evidence: The Semantic Web BIBREF0 , BIBREF1 and the growing popularity of Linked Data (data that are published using Semantic Web technologies) have renewed interest in concept-to-text natural language generation (nlg), especially text generation from ontologies BIBREF2 , BIBREF3 , BIBREF4 , BIBREF5 , BIBREF6 , BIBREF7 , BIBREF8 , BIBREF9 , BIBREF10 . An ontology provides a conceptualization of a knowledge domain (e.g., consumer electronics, diseases) by defining the classes and subclasses of the individuals (entities) in the domain, the possible relations between them etc. The current standard to specify Semantic Web ontologies is owl BIBREF11 , BIBREF12 , a formal language based on description logics BIBREF13 , rdf, and rdf schema BIBREF14 . Given an owl ontology for a knowledge domain, one can publish on the Web machine-readable statements about the domain (e.g., available products, known diseases, their features or symptoms), with the statements having formally defined semantics based on the ontology. nlg can then produce texts describing classes or individuals of the ontology (e.g., product descriptions, information about diseases) from the same statements. This way the same information becomes more easily accessible to both computers (which read the machine-readable statements) and end-users (who read the texts), which is one of the main goals of the Semantic Web.
nlg systems typically employ a pipeline architecture BIBREF15 . Firstly, content selection chooses the logical facts (axioms, in the case of an owl ontology) to be expressed in the text to be generated. The purpose of the next stage, text planning, ranges from simply ordering the facts to be expressed, in effect also ordering the sentences that will express them, to making more complex decisions about the rhetorical structure of the text. Lexicalization then selects the words and syntactic structures to express each fact as a single sentence. Sentence aggregation may then combine shorter sentences into longer ones. Another component generates appropriate referring expressions (pronouns, noun phrases etc.), and surface realization produces the final text, based on internal representations of the previous decisions. Each stage of the pipeline in effect performs a local optimization, constrained by decisions of the previous stages, and largely unaware of the consequences of its own decisions on the subsequent stages.
The pipeline architecture has engineering advantages (e.g., it is easier to specify and monitor the input and output of each stage), but produces texts that may be suboptimal, since the decisions of the generation stages are actually co-dependent BIBREF16 , BIBREF17 , BIBREF18 . Content selection, for example, may greedily select the most important facts among those that are relevant to the purpose of the text, but these facts may require too many words to express, which 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 (facts to express, words and syntactic structures to use) may also lead to more or fewer sentence aggregation opportunities, affecting the length and readability of the texts. Some of these issues can be addressed by over-generating at each stage (e.g., producing several alternative sets of facts at the end of content selection, several alternative lexicalizations etc.) and employing a final ranking component to select the best combination BIBREF19 . This over-generate and rank approach, however, may also fail to find an optimal solution, and generates an exponentially large number of candidate solutions when several components are pipelined.
In this article, we present an Integer Linear Programming (ilp) 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 short product descriptions to be included as advertisements in Web search results BIBREF20 , BIBREF21 . Question answering may also involve generating a natural language summary of facts (e.g., rdf triples) related to a question, without exceeding a maximum text length BIBREF22 ; the more compact the summary, the more facts can be reported in the available space, increasing the chances of reporting the information sought by the user. Compact texts are also desirable when showing texts on devices with small screens BIBREF23 or as subtitles BIBREF24 .
If an importance score is available for each fact, our model can take it into account to maximize the total importance (instead of the total number) of the expressed facts per word. The model itself, however, does not produce importance scores; we assume that the scores are produced by a separate process BIBREF25 , BIBREF26 , not included in our content selection. For simplicity, in the experiments of this article we treat all the facts as equally important. An extended version of our ilp model also considers a limited form of referring expression generation, where the best name must be chosen per individual or class among multiple alternatives. The extended model also avoids sentences that report information that is obvious (to humans) from the names of the individuals and classes (e.g., “A red wine is a kind of wine with red color”). Experiments with three owl ontologies from very different knowledge domains (wines, consumer electronics, diseases) confirm that our models lead to more compact texts, compared to pipeline systems with the same components, with no deterioration or with improvements in the perceived quality of the generated texts. Although solving ilp problems is in general np-hard BIBREF27 , off-the-shelf ilp solvers can be used. The available solvers guarantee finding a globally optimum solution, and they are very fast in practice in the ilp problems we consider, when the the number of available facts (per individual or class being described) is small. We also present an approximation of our ilp models, which is more efficient when the number of available facts is larger and longer texts need to be generated.
Our ilp models (and approximations) have been embedded in Naturalowl BIBREF10 , an nlg system for owl, as alternatives to the system's original pipeline architecture. We base our work on Naturalowl, because it is the only open-source nlg system for owl that implements all the processing stages of a typical nlg system BIBREF15 , it is extensively documented, and has been tested with several ontologies. The processing stages and linguistic resources of Naturalowl are typical of nlg systems BIBREF28 . Hence, we believe that our work is, at least in principle, also applicable to other nlg systems. Our ilp models do not directly consider text planning, but rely on the (external to the ilp model) text planner of Naturalowl. We hope to include more text planning and referring expression generation decisions directly in our ilp model in future work. We also do not consider surface realization, since it is not particularly interesting in Naturalowl; all the decisions have in effect already been made by the time this stage is reached.
The remainder of this article is structured as follows. Section "Background Information about NaturalOWL" below provides background information about Naturalowl. Section "Our Integer Linear Programming Models" defines our ilp models. Section "Computational Complexity and Approximations" discusses the computational complexity of our ilp models, along with the more efficient approximation that can be used when then number of available facts is large. Section "Experiments" presents our experiments. Section "Related Work" discusses previous related work. Section "Conclusions and Future Work" concludes and proposes future work.
In this article, we present an Integer Linear Programming (ilp) 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.
Answer: An Integer Linear Programming (ilp) model.