Abstract
Dialogue summarization is receiving increasing attention from researchers due to its extraordinary difficulty and unique application value. We observe that current dialogue summarization models have flaws that may not be well exposed by frequently used metrics such as ROUGE. In our paper, we re-evaluate 18 categories of metrics in terms of four dimensions: coherence, consistency, fluency and relevance, as well as a unified human evaluation of various models for the first time. Some noteworthy trends which are different from the conventional summarization tasks are identified. We will release DialSummEval, a multi-faceted dataset of human judgments containing the outputs of 14 models on SAMSum.- Anthology ID:
- 2022.naacl-main.418
- Volume:
- Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
- Month:
- July
- Year:
- 2022
- Address:
- Seattle, United States
- Editors:
- Marine Carpuat, Marie-Catherine de Marneffe, Ivan Vladimir Meza Ruiz
- Venue:
- NAACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 5693–5709
- Language:
- URL:
- https://aclanthology.org/2022.naacl-main.418
- DOI:
- 10.18653/v1/2022.naacl-main.418
- Cite (ACL):
- Mingqi Gao and Xiaojun Wan. 2022. DialSummEval: Revisiting Summarization Evaluation for Dialogues. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 5693–5709, Seattle, United States. Association for Computational Linguistics.
- Cite (Informal):
- DialSummEval: Revisiting Summarization Evaluation for Dialogues (Gao & Wan, NAACL 2022)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-2/2022.naacl-main.418.pdf
- Code
- kite99520/dialsummeval
- Data
- DialSummEval