# Energy-based Self-attentive Learning of Abstractive Communities for Spoken Language Understanding

arXiv: https://arxiv.org/abs/1904.09491 · HTML: https://mlsanity.com/paper/1904.09491/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Apr 2019.

## Built on by (most cited first, 4 of 4)

- [Abstractive Meeting Summarization: A Survey](https://mlsanity.com/paper/2208.04163/) (2022)
- [FREDSum: A Dialogue Summarization Corpus for French Political Debates](https://mlsanity.com/paper/2312.04843/) (2023)
- [Leveraging Discourse Structure for Extractive Meeting Summarization](https://mlsanity.com/paper/2405.11055/) (2024)
- [NLP Research and Resources at DaSciM, Ecole Polytechnique](https://mlsanity.com/paper/2112.00566/) (2021)

## Abstract (arXiv)

Abstractive community detection is an important spoken language understanding task, whose goal is to group utterances in a conversation according to whether they can be jointly summarized by a common abstractive sentence. This paper provides a novel approach to this task. We first introduce a neural contextual utterance encoder featuring three types of self-attention mechanisms. We then train it using the siamese and triplet energy-based meta-architectures. Experiments on the AMI corpus show that our system outperforms multiple energy-based and non-energy based baselines from the state-of-the-art. Code and data are publicly available.