Extractive summarization using a latent variable model

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Extractive summarization using a latent variable model
Extractive multi-document summarization is the task of choosing sentences from a set of documents to compose a summary text in response to a user query. We propose a generative approach to explicitly identify summary and non-summary topic distributions in the sentences of a given set of documents (i.e., document cluster). Using these approximate summary topic probabilities as latent output variables, we build a discriminative classifier model. The sentences in new document clusters are inferred using the trained discriminative model. In our experiments we find that the proposed summarization approach is effective in comparison to the state-of-the-art methods.
Asli Çelikyilmaz, Dilek Hakkani-Tür
Added 18 May 2011
Updated 18 May 2011
Type Journal
Year 2010
Authors Asli Çelikyilmaz, Dilek Hakkani-Tür
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