Abstract
We investigate pre-training techniques for abstractive multi-document summarization (MDS), which is much less studied than summarizing single documents. Though recent work has demonstrated the effectiveness of highlighting information salience for pre-training strategy design, they struggle to generate abstractive and reflective summaries, which are critical properties for MDS. To this end, we present **PELMS**, a pre-trained model that uses pre-training objectives based on semantic coherence heuristics and faithfulness constraints together with unlabeled multi-document inputs, to promote the generation of concise, fluent, and faithful summaries. To support the training of PELMS, we compile **MultiPT**, a multi-document pre-training corpus containing over 93 million documents to form more than 3million unlabeled topic-centric document clusters, covering diverse genres such as product reviews, news, and general knowledge. We perform extensive evaluation of PELMS in low-shot settings on a wide range of MDS datasets. Our approach consistently outperforms competitive comparisons with respect to overall informativeness, abstractiveness, coherence, and faithfulness, and with minimal fine-tuning can match performance of language models at a much larger scale (e.g., GPT-4).- Anthology ID:
- 2024.naacl-long.423
- Volume:
- Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)
- Month:
- June
- Year:
- 2024
- Address:
- Mexico City, Mexico
- Editors:
- Kevin Duh, Helena Gomez, Steven Bethard
- Venue:
- NAACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 7652–7674
- Language:
- URL:
- https://preview.aclanthology.org/add_missing_videos/2024.naacl-long.423/
- DOI:
- 10.18653/v1/2024.naacl-long.423
- Cite (ACL):
- Joseph Peper, Wenzhao Qiu, and Lu Wang. 2024. PELMS: Pre-training for Effective Low-Shot Multi-Document Summarization. In Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers), pages 7652–7674, Mexico City, Mexico. Association for Computational Linguistics.
- Cite (Informal):
- PELMS: Pre-training for Effective Low-Shot Multi-Document Summarization (Peper et al., NAACL 2024)
- PDF:
- https://preview.aclanthology.org/add_missing_videos/2024.naacl-long.423.pdf