Detecting Discrepancies and Improving Intelligibility: Two Preliminary Evaluations of RIPTIDES

Michael White, Claire Cardie, Vincent Ng, Kiri Wagstaff, and Daryl McCullough.
2001 Document Understanding Conference (DUC), 2001.

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Abstract

We report on two preliminary evaluations of RIPTIDES, a system that combines information extraction (IE), extraction-based summarization, and natural language generation to support user directed multidocument summarization. We report first on a case study of the system's ability to detect discrepancies in numerical estimates appearing in different new articles at different time points in the evolution of a story using a corpus of more than 100 articles from multiple sources about an earthquake in Central America in January 2001. We then report on how our domain-independent extraction-based summarizer performed on the DUC multidocument task, discussing the extent to which we were able to improve cohesion and organization over the baseline, without unduly sacrificing content relevance.