Generating Multiple-Length Summaries via Reinforcement Learning for Unsupervised Sentence Summarization
Dongmin Hyun, Xiting Wang, Chayoung Park, Xing Xie, Hwanjo Yu
Abstract
Sentence summarization shortens given texts while maintaining core contents of the texts. Unsupervised approaches have been studied to summarize texts without ground-truth summaries. However, recent unsupervised models are extractive, which remove words from texts and thus they are less flexible than abstractive summarization. In this work, we devise an abstractive model based on reinforcement learning without ground-truth summaries. We formulate the unsupervised summarization based on the Markov decision process with rewards representing the summary quality. To further enhance the summary quality, we develop a multi-summary learning mechanism that generates multiple summaries with varying lengths for a given text, while making the summaries mutually enhance each other. Experimental results show that the proposed model substantially outperforms both abstractive and extractive models, yet frequently generating new words not contained in input texts.- Anthology ID:
- 2022.findings-emnlp.214
- Volume:
- Findings of the Association for Computational Linguistics: EMNLP 2022
- Month:
- December
- Year:
- 2022
- Address:
- Abu Dhabi, United Arab Emirates
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 2939–2951
- Language:
- URL:
- https://aclanthology.org/2022.findings-emnlp.214
- DOI:
- Cite (ACL):
- Dongmin Hyun, Xiting Wang, Chayoung Park, Xing Xie, and Hwanjo Yu. 2022. Generating Multiple-Length Summaries via Reinforcement Learning for Unsupervised Sentence Summarization. In Findings of the Association for Computational Linguistics: EMNLP 2022, pages 2939–2951, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.
- Cite (Informal):
- Generating Multiple-Length Summaries via Reinforcement Learning for Unsupervised Sentence Summarization (Hyun et al., Findings 2022)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/2022.findings-emnlp.214.pdf