# Abstractive Meeting Summarization UsingDependency Graph Fusion

arXiv: https://arxiv.org/abs/1609.07035 · HTML: https://mlsanity.com/paper/1609.07035/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Sep 2016.

## Built on by (most cited first, 6 of 6)

- [A Survey on Dialogue Summarization: Recent Advances and New Frontiers](https://mlsanity.com/paper/2107.03175/) (2021)
- [TWEETSUMM -- A Dialog Summarization Dataset for Customer Service](https://mlsanity.com/paper/2111.11894/) (2021)
- [Controllable Abstractive Dialogue Summarization with Sketch Supervision](https://mlsanity.com/paper/2105.14064/) (2021)
- [Meeting Summarization: A Survey of the State of the Art](https://mlsanity.com/paper/2212.08206/) (2022)
- [Modeling Unified Semantic Discourse Structure for High-quality Headline Generation](https://mlsanity.com/paper/2403.15776/) (2024)
- [Extra Global Attention Designation Using Keyword Detection in Sparse Transformer Architectures](https://mlsanity.com/paper/2410.08971/) (2024)

## Abstract (arXiv)

Automatic summarization techniques on meeting conversations developed so far have been primarily extractive, resulting in poor summaries. To improve this, we propose an approach to generate abstractive summaries by fusing important content from several utterances. Any meeting is generally comprised of several discussion topic segments. For each topic segment within a meeting conversation, we aim to generate a one sentence summary from the most important utterances using an integer linear programming-based sentence fusion approach. Experimental results show that our method can generate more informative summaries than the baselines.