Abstract
Although Large Language Models (LLMs) are successful in abstractive summarization of short dialogues, summarization of long dialogues remains challenging. To address this challenge, we propose a novel algorithm that processes complete dialogues comprising thousands of tokens into topic-segment-level Abstract Meaning Representation (AMR) graphs, which explicitly capture the dialogue structure, highlight salient semantics, and preserve high-level information. We also develop a new text-graph attention to leverage both graph semantics and a pretrained LLM that exploits the text. Finally, we propose an AMR node selection loss used jointly with conventional cross-entropy loss, to create additional training signals that facilitate graph feature encoding and content selection. Experiments show that our system outperforms the state-of-the-art models on multiple long dialogue summarization datasets, especially in low-resource settings, and generalizes well to out-of-domain data.- Anthology ID:
- 2023.findings-acl.871
- Volume:
- Findings of the Association for Computational Linguistics: ACL 2023
- Month:
- July
- Year:
- 2023
- Address:
- Toronto, Canada
- Editors:
- Anna Rogers, Jordan Boyd-Graber, Naoaki Okazaki
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 13851–13883
- Language:
- URL:
- https://aclanthology.org/2023.findings-acl.871
- DOI:
- 10.18653/v1/2023.findings-acl.871
- Cite (ACL):
- Yilun Hua, Zhaoyuan Deng, and Kathleen McKeown. 2023. Improving Long Dialogue Summarization with Semantic Graph Representation. In Findings of the Association for Computational Linguistics: ACL 2023, pages 13851–13883, Toronto, Canada. Association for Computational Linguistics.
- Cite (Informal):
- Improving Long Dialogue Summarization with Semantic Graph Representation (Hua et al., Findings 2023)
- PDF:
- https://preview.aclanthology.org/ingest-2024-clasp/2023.findings-acl.871.pdf