A Response to the Need for Summary Responses

A Response to the Need for Summary Responses J.K. Kalita, M.J. Colbourn + and G.I. McCalla Department of Computational Science University of Saskatchewan Saskatoon, Saskatchewan, STN 0W0 CANADA Abstract In this paper we argue that natural language inter- faces to databases should be able to produce summary responses as well as listing actual data. We describe a system (incorporating a number of heuristics and a knowledge base built on top of the database) that has been developed to generate such summary responses. It is largely domain-independent, has been tested on many examples, and handles a wide variety of situa- tions where summary responses would be useful. 1. Introduction For over a decade research has been ongoing into the diverse and complex issues involved in developing smart natural language interfaces to database systems. Pioneering front-end systems such as PLANES [15], REQUEST [121, TORUS [11] and RENDEZVOUS [1] experimented with, among other things, various parsing formalisms (e.g. semantic grammars, transformational grammars and auglmented transition networks); the need for knowledge representation (e.g. using produc- tion systems or semantic networks); and the usefulness of clarification dialogue in disambiguating a user's query. Recent research has addressed various dialogue issues in order to enhance the elegance of the database interactions. Such research includes attempts to resolve anaphoric references in queries [2,4,14,18], to track the user's focus of attention [2,4,14,18], and to generate cooperative responses. In particular, the CO-OP sys- tem [7] is able to analyze presumptions of the user in order to generate appropriate explanations for answers that may mislead the user. Janas [5] takes a similar approach to generate indirect answers instead of pro- viding direct inappropriate ones. Mays [8] has developed techniques to monitor changes in the data- base and provide relevant information on these changes to the user. McCoy [9] and McKeown [10] attempt to provide answers to questions about the structure of the database rather than extensional information as to its contents. We investigate herein, one particular approach to generating "non-extensional" responses - in particular the generation of "summary" responses. Generating abstract "summary" responses to users' queries is often preferable to providing enumerative replies. This follows from an important convention of human dialogue that no participant should monopolize the discourse (i.e. "be brief" [3]). Furthermore, exten- sional responses can occasionally mislead the user where summary responses would not. Consider the following example [13]: QI: Which department managers earn over $40k per year? SI-I: Abel, Baker, Charles, Doug. SI-2: All of them. By enumerating managers who earn over $40k, the fizst response implies that there are managers who do not earn that much. In linguistic pragmatics, this is called a scalar implicature [3]. In circumstances where the user is liable to infer an invalid scalar implicature, the sys- tem should be able to produce an appropriate response to block the generation of such an inference as is done by the response $1-2. 2. Overview of the System We describe herein a system which has been developed for the generation of summary responses to …

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