Abstract
We report the results of DialogSum Challenge, the shared task on summarizing real-life sce- nario dialogues at INLG 2022. Four teams participate in this shared task and three submit their system reports, exploring different meth- ods to improve the performance of dialogue summarization. Although there is a great im- provement over the baseline models regarding automatic evaluation metrics, such as ROUGE scores, we find that there is a salient gap be- tween model generated outputs and human an- notated summaries by human evaluation from multiple aspects. These findings demonstrate the difficulty of dialogue summarization and suggest that more fine-grained evaluatuion met- rics are in need.- Anthology ID:
- 2022.inlg-genchal.14
- Volume:
- Proceedings of the 15th International Conference on Natural Language Generation: Generation Challenges
- Month:
- July
- Year:
- 2022
- Address:
- Waterville, Maine, USA and virtual meeting
- Editors:
- Samira Shaikh, Thiago Ferreira, Amanda Stent
- Venue:
- INLG
- SIG:
- SIGGEN
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 94–103
- Language:
- URL:
- https://aclanthology.org/2022.inlg-genchal.14
- DOI:
- Cite (ACL):
- Yulong Chen, Naihao Deng, Yang Liu, and Yue Zhang. 2022. DialogSum Challenge: Results of the Dialogue Summarization Shared Task. In Proceedings of the 15th International Conference on Natural Language Generation: Generation Challenges, pages 94–103, Waterville, Maine, USA and virtual meeting. Association for Computational Linguistics.
- Cite (Informal):
- DialogSum Challenge: Results of the Dialogue Summarization Shared Task (Chen et al., INLG 2022)
- PDF:
- https://preview.aclanthology.org/improve-issue-templates/2022.inlg-genchal.14.pdf