1. Unsupervised Topic Modeling Approaches to Decision Summarization in Spoken Meetings. (arXiv:1606.07829v1 [cs.CL])

    We present a token-level decision summarization framework that utilizes the latent topic structures of utterances to identify "summary-worthy" words. Concretely, a series of unsupervised topic models is explored and experimental results show that fine-grained topic models, which discover topics at the utterance-level rather than the document-level, can better identify the gist of the decision-making process.

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      Discourse, Entailment, Machine Translation, NER, Parsing, Segmentation, Semantic, Sentiment, Summarization, WSD
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