ForumSum: A Multi-Speaker Conversation Summarization Dataset

Misha Khalman, Yao Zhao, Mohammad Saleh


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
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
Bibkey:
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)
Copy Citation:
PDF:
https://aclanthology.org/2021.findings-emnlp.391.pdf
Data
SAMSum Corpus