Our research aims at interactive document viewers that can select and highlight relevant text passages on demand. Another related objective is the generation of topic-specific summaries of texts as opposed to general purpose summaries. This paper introduces our notions of discourse structure tree and level-of-detail tree. Both structures are used to represent relevant aspects of a text segment for the above mentioned purposes. Furthermore, we introduce a Knowledge Acquisition Framework for DIScourse processing (KAFDIS) that allows the incremental and efficient acquisition of knowledge for the reliable construction of the discourse structure graph and the level-of-detail tree based on cue phrases. An effective knowledge acquisition process is crucial to allow the economical development of systems that can handle a large variety of topics. Our knowledge acquisition approach ensures always a consistent knowledge base whose semantics are well controlled by the expert. It is an incremental approach that allows patching of the knowledge base as soon as the need arises without causing any inconsistencies. We also present promising experimental results with our approach.
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