Attention, intentions, and the structure of discourse
Computational Linguistics
Automatic text structuring and summarization
Information Processing and Management: an International Journal - Special issue: methods and tools for the automatic construction of hypertext
Summarizing Similarities and Differences Among Related Documents
Information Retrieval
The rhetorical parsing, summarization, and generation of natural language texts
The rhetorical parsing, summarization, and generation of natural language texts
The rhetorical parsing, summarization, and generation of natural language texts
The rhetorical parsing, summarization, and generation of natural language texts
An unsupervised approach to recognizing discourse relations
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Building a discourse-tagged corpus in the framework of Rhetorical Structure Theory
SIGDIAL '01 Proceedings of the Second SIGdial Workshop on Discourse and Dialogue - Volume 16
Automated text summarization and the SUMMARIST system
TIPSTER '98 Proceedings of a workshop on held at Baltimore, Maryland: October 13-15, 1998
Pooling commentaries on legal texts from lawyers and experts in a collaborative work
Proceedings of the 3rd international conference on Knowledge capture
Two decades of ripple down rules research
The Knowledge Engineering Review
Combining different summarization techniques for legal text
HYBRID '12 Proceedings of the Workshop on Innovative Hybrid Approaches to the Processing of Textual Data
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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.