A Multi-Document Coverage Reward for RELAXed Multi-Document Summarization

Jacob Parnell, Inigo Jauregi Unanue, Massimo Piccardi


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
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
Bibkey:
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)
Copy Citation:
PDF:
https://aclanthology.org/2022.acl-long.351.pdf
Code
 jacob-parnell-rozetta/longformer_coverage
Data
Multi-NewsWCEP