Abstract
Systematic literature reviews in the biomedical space are often expensive to conduct. Automation through machine learning and large language models could improve the accuracy and research outcomes from such reviews. In this study, we evaluate a pre-trained LongT5 model on the MSLR22: Multi-Document Summarization for Literature Reviews Shared Task datasets. We weren’t able to make any improvements on the dataset benchmark, but we do establish some evidence that current summarization metrics are insufficient in measuring summarization accuracy. A multi-document summarization web tool was also built to demonstrate the viability of summarization models for future investigators: https://ben-yu.github.io/summarizer- Anthology ID:
- 2022.sdp-1.22
- Volume:
- Proceedings of the Third Workshop on Scholarly Document Processing
- Month:
- October
- Year:
- 2022
- Address:
- Gyeongju, Republic of Korea
- Editors:
- Arman Cohan, Guy Feigenblat, Dayne Freitag, Tirthankar Ghosal, Drahomira Herrmannova, Petr Knoth, Kyle Lo, Philipp Mayr, Michal Shmueli-Scheuer, Anita de Waard, Lucy Lu Wang
- Venue:
- sdp
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 188–192
- Language:
- URL:
- https://aclanthology.org/2022.sdp-1.22
- DOI:
- Cite (ACL):
- Benjamin Yu. 2022. Evaluating Pre-Trained Language Models on Multi-Document Summarization for Literature Reviews. In Proceedings of the Third Workshop on Scholarly Document Processing, pages 188–192, Gyeongju, Republic of Korea. Association for Computational Linguistics.
- Cite (Informal):
- Evaluating Pre-Trained Language Models on Multi-Document Summarization for Literature Reviews (Yu, sdp 2022)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2022.sdp-1.22.pdf