Eulerian at B io L ay S umm: Preprocessing Over Abstract is All You Need (2024)

Abstract: In this paper, we present our approach to the BioLaySumm 2024 Shared Task on Lay Sum- marization of Biomedical Research Articles at BioNLP workshop 2024. The task aims to generate lay summaries from the abstract and main texts of biomedical research articles, making them understandable to lay audiences. We used some preprocessing techniques and finetuned FLAN-T5 models for the summarization task. Our method achieved an AlignScore of 0.9914 and a SummaC metric score of 0.944.

## introduction research in every domain has increased significantly, making it challenging for cross-domain researchers to keep track of terminologies outside their expertise. providing layman summarization in biomedical research addresses this issue. this task is particularly important given the growing volume of biomedical literature, which makes manual summarization impractical. automated lay summarization can significantly enhance the reach and impact of scientific findings by making them accessible to a wider audience, including patients, healthcare providers, policymakers, and the general public. the biolaysumm 2024 shared task on lay summarization of biomedical research articles is designed to advance the development of automated systems capable of generating accurate and coherent lay summaries from biomedical articles. this task utilizes two separate datasets, focusing on generating summaries that maintain the essence and factuality of the original research while being understandable to a lay audience. ## related work past works in summarisation has been along two directions: extractive summarisation and abstractive summarisation. extractive summarisation involves selecting and extracting key phrases, sentences, or segments directly from the original text to create a summary while in abstractive summarisation the summary is generated by creating new sentences that convey the key information from the original text. recent works like pegasus @xcite uses transformer like models with a self supervised objective for summarisation. in recent years, most of the work on abstractive summarisation has been based on treating the task as a sequence-to-sequence task and using pretrained encoders @xcite . in this work, we explore on the usage of llms for biomedical articles summarisation. specifically, we use flan-t5 model @xcite for finetuning it for our use case by treating summarisation as a sequence-to-sequence task. ## datasets the task included two datasets, plos and elife @xcite . plos is the larger dataset derived from public library of science, comprising 24,773 instances for training and 1,376 for validation whereas the elife dataset was derived from the peer-reviewed elife journal and it contains 4,346 instances for training and 241 for validation. the test data used for evaluation consisted of 142 articles each of plos and elife datasets. ## conclusion the comparative analysis of various flan-t5 models and the poa technique for summarizing biomedical research articles has yielded insightful findings. the flan-t5 small model showed enhanced relevance and readability metrics when trained on combined plos and elife datasets, though at the expense of factuality. the flan-t5 base model further improved relevance and readability metrics but also compromised factuality. introducing a cosine learning rate scheduler to the flan-t5 base model improved readability and factuality metrics, indicating a better balance in model performance. notably, the poa technique, despite not involving any training, outperformed all flan-t5 models in relevance and factuality metrics, demonstrating its effectiveness in generating accurate and concise summaries from abstracts. these results underscore the importance of training strategies in developing effective summarization models, while also highlighting the potential of simple preprocessing techniques like poa.