Simple Conversational Data Augmentation for Semi-supervised Abstractive Dialogue Summarization

Jiaao Chen, Diyi Yang


Abstract
Abstractive conversation summarization has received growing attention while most current state-of-the-art summarization models heavily rely on human-annotated summaries. To reduce the dependence on labeled summaries, in this work, we present a simple yet effective set of Conversational Data Augmentation (CODA) methods for semi-supervised abstractive conversation summarization, such as random swapping/deletion to perturb the discourse relations inside conversations, dialogue-acts-guided insertion to interrupt the development of conversations, and conditional-generation-based substitution to substitute utterances with their paraphrases generated based on the conversation context. To further utilize unlabeled conversations, we combine CODA with two-stage noisy self-training where we first pre-train the summarization model on unlabeled conversations with pseudo summaries and then fine-tune it on labeled conversations. Experiments conducted on the recent conversation summarization datasets demonstrate the effectiveness of our methods over several state-of-the-art data augmentation baselines.
Anthology ID:
2021.emnlp-main.530
Volume:
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
Month:
November
Year:
2021
Address:
Online and Punta Cana, Dominican Republic
Editors:
Marie-Francine Moens, Xuanjing Huang, Lucia Specia, Scott Wen-tau Yih
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
6605–6616
Language:
URL:
https://aclanthology.org/2021.emnlp-main.530
DOI:
10.18653/v1/2021.emnlp-main.530
Bibkey:
Cite (ACL):
Jiaao Chen and Diyi Yang. 2021. Simple Conversational Data Augmentation for Semi-supervised Abstractive Dialogue Summarization. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 6605–6616, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.
Cite (Informal):
Simple Conversational Data Augmentation for Semi-supervised Abstractive Dialogue Summarization (Chen & Yang, EMNLP 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.emnlp-main.530.pdf
Video:
 https://aclanthology.org/2021.emnlp-main.530.mp4
Code
 gt-salt/coda
Data
SAMSum