Zero-Shot Aspect-Based Scientific Document Summarization using Self-Supervised Pre-training
Amir Soleimani, Vassilina Nikoulina, Benoit Favre, Salah Ait Mokhtar
Abstract
We study the zero-shot setting for the aspect-based scientific document summarization task. Summarizing scientific documents with respect to an aspect can remarkably improve document assistance systems and readers experience. However, existing large-scale datasets contain a limited variety of aspects, causing summarization models to over-fit to a small set of aspects and a specific domain. We establish baseline results in zero-shot performance (over unseen aspects and the presence of domain shift), paraphrasing, leave-one-out, and limited supervised samples experimental setups. We propose a self-supervised pre-training approach to enhance the zero-shot performance. We leverage the PubMed structured abstracts to create a biomedical aspect-based summarization dataset. Experimental results on the PubMed and FacetSum aspect-based datasets show promising performance when the model is pre-trained using unlabelled in-domain data.- Anthology ID:
- 2022.bionlp-1.5
- Volume:
- Proceedings of the 21st Workshop on Biomedical Language Processing
- Month:
- May
- Year:
- 2022
- Address:
- Dublin, Ireland
- Editors:
- Dina Demner-Fushman, Kevin Bretonnel Cohen, Sophia Ananiadou, Junichi Tsujii
- Venue:
- BioNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 49–62
- Language:
- URL:
- https://aclanthology.org/2022.bionlp-1.5
- DOI:
- 10.18653/v1/2022.bionlp-1.5
- Cite (ACL):
- Amir Soleimani, Vassilina Nikoulina, Benoit Favre, and Salah Ait Mokhtar. 2022. Zero-Shot Aspect-Based Scientific Document Summarization using Self-Supervised Pre-training. In Proceedings of the 21st Workshop on Biomedical Language Processing, pages 49–62, Dublin, Ireland. Association for Computational Linguistics.
- Cite (Informal):
- Zero-Shot Aspect-Based Scientific Document Summarization using Self-Supervised Pre-training (Soleimani et al., BioNLP 2022)
- PDF:
- https://preview.aclanthology.org/emnlp22-frontmatter/2022.bionlp-1.5.pdf
- Code
- asoleimanib/zeroshotaspectbased
- Data
- FacetSum