LLM aided semi-supervision for efficient Extractive Dialog Summarization
Nishant Mishra, Gaurav Sahu, Iacer Calixto, Ameen Abu-Hanna, Issam Laradji
Abstract
Generating high-quality summaries for chat dialogs often requires large labeled datasets. We propose a method to efficiently use unlabeled data for extractive summarization of customer-agent dialogs. In our method, we frame summarization as a question-answering problem and use state-of-the-art large language models (LLMs) to generate pseudo-labels for a dialog. We then use these pseudo-labels to fine-tune a chat summarization model, effectively transferring knowledge from the large LLM into a smaller specialized model. We demonstrate our method on the TWEETSUMM dataset, and show that using 10% of the original labelled data set we can achieve 65.9/57.0/61.0 ROUGE-1/-2/-L, whereas the current state-of-the-art trained on the entire training data set obtains 65.16/55.81/64.37 ROUGE-1/-2/-L. In other words, in the worst case (i.e., ROUGE-L) we still effectively retain 94.7% of the performance while using only 10% of the data.- Anthology ID:
- 2023.findings-emnlp.670
- Volume:
- Findings of the Association for Computational Linguistics: EMNLP 2023
- Month:
- December
- Year:
- 2023
- Address:
- Singapore
- Editors:
- Houda Bouamor, Juan Pino, Kalika Bali
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 10002–10009
- Language:
- URL:
- https://aclanthology.org/2023.findings-emnlp.670
- DOI:
- 10.18653/v1/2023.findings-emnlp.670
- Cite (ACL):
- Nishant Mishra, Gaurav Sahu, Iacer Calixto, Ameen Abu-Hanna, and Issam Laradji. 2023. LLM aided semi-supervision for efficient Extractive Dialog Summarization. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 10002–10009, Singapore. Association for Computational Linguistics.
- Cite (Informal):
- LLM aided semi-supervision for efficient Extractive Dialog Summarization (Mishra et al., Findings 2023)
- PDF:
- https://preview.aclanthology.org/ingest-2024-clasp/2023.findings-emnlp.670.pdf