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In this paper we argue that natural language interfaces 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 situations where summary responses would be useful. | 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 S7N owo CANADA Abstract In this paper we argue that natural language interfaces 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 situations 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 12 TORUS 11 and RENDEZVOUS 1 experimented with among other things various parsing formalisms e.g. semantic grammars transformational grammars and augmented transition networks the need for knowledge representation e.g. using production 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 16 to track the user s focus of attention 2 4 14 16 and to generate cooperative responses. In particular the CO-OP system 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 providing direct inappropriate ones. Mays 8 has developed techniques to monitor changes in the database and provide relevant information on these changes to the user. McCoy 9 and McKeown 10 attempt to provide answers to questions about the
