Unsupervised Topic Modeling Approaches to Decision Summarization in Spoken Meetings [article]

Lu Wang, Claire Cardie
2016 arXiv   pre-print
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. Moreover, our proposed token-level summarization approach, which is able to remove
more » ... dancies within utterances, outperforms existing utterance ranking based summarization methods. Finally, context information is also investigated to add additional relevant information to the summary.
arXiv:1606.07829v1 fatcat:drbyg72pfvbgxnetlgudyzfbre