Unsupervised Topic Modeling Approaches to Decision Summarization in Spoken Meetings
This work addresses the need for efficient summarization of spoken meetings, particularly for decision-making processes, though it is incremental as it builds on existing topic modeling approaches.
The paper tackles the problem of summarizing decision-making in spoken meetings by proposing a token-level framework that uses fine-grained, utterance-level topic models to identify key words, achieving better performance than existing utterance ranking methods.
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 redundancies within utterances, outperforms existing utterance ranking based summarization methods. Finally, context information is also investigated to add additional relevant information to the summary.