tailieunhanh - Báo cáo khoa học: "Robust Generic and Query-based Summarisation"

We present a robust summarisation system developed within the GATE architecture that makes use of robust components for semantic tagging and coreference resolution provided by GATE. Our system combines GATE components with well established statistical techniques developed for the purpose of text summarisation research. The system supports "generic" and query-based summarisation addressing the need for user adaptation. Here, we present a summarisation system that makes use of robust components for semantic tagging and coreference resolution provided by GATE (Cunningham et al., 2002). . | Robust Generic and Query-based Summarisation Horacio Saggion Kalina Bontcheva Hamish Cunningham Department of Computer Science University of Sheffield 211 Portobello Street - Sheffield - SI 4DP England - United Kingdom saggion kalina hamish @ Abstract We present a robust summarisation system developed within the GATE architecture that makes use of robust components for semantic tagging and coreference resolution provided by GATE. Our system combines GATE components with well established statistical techniques developed for the purpose of text summarisation research. The system supports generic and query-based summarisation addressing the need for user adaptation. 1 Introduction Two approaches are generally considered in automatic text summarisation research the shallow sentence extraction approach and the deep understand and generate approach Mani 2000 . Sentence extraction methods are quite robust but sentence extracts suffer from lack of cohesion and coherence. Methods that identify the essential information of the document by either information extraction or text understanding and that use the key information to produce a new text lead to high-quality summarisation Paice and Jones 1993 Saggion and Lapalme 2002 but suffer from the knowledge-bottleneck problem adapting information extraction rules templates and generation grammars to new tasks or domains is time consuming. An alternative to these approaches is to use combination of robust techniques for semantic tagging together with statistical methods Saggion 2002 . Here we present a summarisation system that makes use of robust components for semantic tagging and coreference resolution provided by GATE Cunningham et al. 2002 . Our system combines GATE components with well established statistical techniques developed for the purpose of text summarisation. The result is the sentence extraction system shown in Figure 1 the relevant sentences of the document are highlighted in the GATE user interface.