Abstract
This paper is about the extractive summarization of meeting speech, using the ICSI and AMI corpora. In the first set of experiments we use prosodic, lexical, structural and speaker-related features to select the most informative dialogue acts from each meeting, with the hypothesis being that such a rich mixture of features will yield the best results. In the second part, we present an approach in which the identification of “meta-comments” is used to create more informative summaries that provide an increased level of abstraction. We find that the inclusion of these meta comments improves summarization performance according to several evaluation metrics.
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Murray, G., Renals, S. (2008). Meta Comments for Summarizing Meeting Speech. In: Popescu-Belis, A., Stiefelhagen, R. (eds) Machine Learning for Multimodal Interaction. MLMI 2008. Lecture Notes in Computer Science, vol 5237. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-85853-9_22
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DOI: https://doi.org/10.1007/978-3-540-85853-9_22
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