# Unsupervised Topic Modeling Approaches to Decision Summarization in Spoken Meetings

arXiv: https://arxiv.org/abs/1606.07829 · HTML: https://mlsanity.com/paper/1606.07829/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Jun 2016.

## Built on by (most cited first, 1 of 1)

- [Topic Modeling Based Extractive Text Summarization](https://mlsanity.com/paper/2106.15313/) (2021)

## Abstract (arXiv)

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.