2006 · 20 citations · 12 references
ComputingComputer ScienceComputational LinguisticsNatural Language ProcessingText AnalyticsData SummarizationSemi-supervised LearningText MiningText StructureText SummarizationTopic ModelingDocument SummarizationMulti-document SummarizationNews SummarizationSemi-supervised ClassificationTopic Bias ControlTopic Discovery
In this paper, we propose a sentence ordering algorithm using a semi-supervised sentence classification and historical ordering strategy. The classification is based on the manifold structure underlying sentences, addressing the problem of limited labeled data. The historical ordering helps to ensure topic continuity and avoid topic bias. Experiments demonstrate that the method is effective.
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Learning with Local and Global Consistency
Dengyong Zhou, Olivier Bousquet, Thomas Navin Lal et al. · MPG.PuRe (Max Planck Society) · 2003 · 3.8K citations · Full text
Building Natural-Language Generation Systems
Ehud Reiter · ArXiv.org · 1996 · 1.3K citations · Full text
Partially labeled classification with Markov random walks
Martin Szummer, Tommi Jaakkola · 2001 · 563 citations