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
- 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
- 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)
- PDF:
- https://preview.aclanthology.org/paclic-22-ingestion/2021.emnlp-main.530.pdf
- Code
- gt-salt/coda
- Data
- SAMSum Corpus