Abstract
Lay summarisation aims at generating a summary for non-expert audience which allows them to keep updated with latest research in a specific field. Despite the significant advancements made in the field of text summarisation, lay summarisation remains relatively under-explored. We present a comprehensive set of experiments and analysis to investigate the effectiveness of existing pre-trained language models in generating lay summaries. When evaluate our models using a BioNLP Shared Task, BioLaySumm, our submission ranked second for the relevance criteria and third overall among 21 competing teams.- Anthology ID:
- 2023.bionlp-1.68
- Volume:
- The 22nd Workshop on Biomedical Natural Language Processing and BioNLP Shared Tasks
- Month:
- July
- Year:
- 2023
- Address:
- Toronto, Canada
- Editors:
- Dina Demner-fushman, Sophia Ananiadou, Kevin Cohen
- Venue:
- BioNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 629–635
- Language:
- URL:
- https://aclanthology.org/2023.bionlp-1.68
- DOI:
- 10.18653/v1/2023.bionlp-1.68
- Cite (ACL):
- Mong Yuan Sim, Xiang Dai, Maciej Rybinski, and Sarvnaz Karimi. 2023. CSIRO Data61 Team at BioLaySumm Task 1: Lay Summarisation of Biomedical Research Articles Using Generative Models. In The 22nd Workshop on Biomedical Natural Language Processing and BioNLP Shared Tasks, pages 629–635, Toronto, Canada. Association for Computational Linguistics.
- Cite (Informal):
- CSIRO Data61 Team at BioLaySumm Task 1: Lay Summarisation of Biomedical Research Articles Using Generative Models (Sim et al., BioNLP 2023)
- PDF:
- https://preview.aclanthology.org/emnlp-22-attachments/2023.bionlp-1.68.pdf