Abstract
Multi-document summarization (MDS) has made significant progress in recent years, in part facilitated by the availability of new, dedicated datasets and capacious language models. However, a standing limitation of these models is that they are trained against limited references and with plain maximum-likelihood objectives. As for many other generative tasks, reinforcement learning (RL) offers the potential to improve the training of MDS models; yet, it requires a carefully-designed reward that can ensure appropriate leverage of both the reference summaries and the input documents. For this reason, in this paper we propose fine-tuning an MDS baseline with a reward that balances a reference-based metric such as ROUGE with coverage of the input documents. To implement the approach, we utilize RELAX (Grathwohl et al., 2018), a contemporary gradient estimator which is both low-variance and unbiased, and we fine-tune the baseline in a few-shot style for both stability and computational efficiency. Experimental results over the Multi-News and WCEP MDS datasets show significant improvements of up to +0.95 pp average ROUGE score and +3.17 pp METEOR score over the baseline, and competitive results with the literature. In addition, they show that the coverage of the input documents is increased, and evenly across all documents.- Anthology ID:
- 2022.acl-long.351
- Volume:
- Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
- Month:
- May
- Year:
- 2022
- Address:
- Dublin, Ireland
- Editors:
- Smaranda Muresan, Preslav Nakov, Aline Villavicencio
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 5112–5128
- Language:
- URL:
- https://aclanthology.org/2022.acl-long.351
- DOI:
- 10.18653/v1/2022.acl-long.351
- Cite (ACL):
- Jacob Parnell, Inigo Jauregi Unanue, and Massimo Piccardi. 2022. A Multi-Document Coverage Reward for RELAXed Multi-Document Summarization. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 5112–5128, Dublin, Ireland. Association for Computational Linguistics.
- Cite (Informal):
- A Multi-Document Coverage Reward for RELAXed Multi-Document Summarization (Parnell et al., ACL 2022)
- PDF:
- https://preview.aclanthology.org/proper-vol2-ingestion/2022.acl-long.351.pdf
- Code
- jacob-parnell-rozetta/longformer_coverage
- Data
- Multi-News, WCEP