Abstract
Abstractive summarization quality had large improvements since recent language pretraining techniques. However, currently there is a lack of datasets for the growing needs of conversation summarization applications. Thus we collected ForumSum, a diverse and high-quality conversation summarization dataset with human written summaries. The conversations in ForumSum dataset are collected from a wide variety of internet forums. To make the dataset easily expandable, we also release the process of dataset creation. Our experiments show that models trained on ForumSum have better zero-shot and few-shot transferability to other datasets than the existing large chat summarization dataset SAMSum. We also show that using a conversational corpus for pre-training improves the quality of the chat summarization model.- Anthology ID:
- 2021.findings-emnlp.391
- Volume:
- Findings of the Association for Computational Linguistics: EMNLP 2021
- Month:
- November
- Year:
- 2021
- Address:
- Punta Cana, Dominican Republic
- Editors:
- Marie-Francine Moens, Xuanjing Huang, Lucia Specia, Scott Wen-tau Yih
- Venue:
- Findings
- SIG:
- SIGDAT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 4592–4599
- Language:
- URL:
- https://aclanthology.org/2021.findings-emnlp.391
- DOI:
- 10.18653/v1/2021.findings-emnlp.391
- Cite (ACL):
- Misha Khalman, Yao Zhao, and Mohammad Saleh. 2021. ForumSum: A Multi-Speaker Conversation Summarization Dataset. In Findings of the Association for Computational Linguistics: EMNLP 2021, pages 4592–4599, Punta Cana, Dominican Republic. Association for Computational Linguistics.
- Cite (Informal):
- ForumSum: A Multi-Speaker Conversation Summarization Dataset (Khalman et al., Findings 2021)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-1/2021.findings-emnlp.391.pdf
- Data
- SAMSum, SummScreen