IKM _ L ab at B io L ay S umm Task 1: Longformer-based Prompt Tuning for Biomedical Lay Summary Generation (2023)

Abstract: This paper describes the entry by the Intelligent Knowledge Management (IKM) Laboratory in the BioLaySumm 2023 task1. We aim to transform lengthy biomedical articles into concise, reader-friendly summaries that can be easily comprehended by the general public. We utilized a long-text abstractive summarization longformer model and experimented with several prompt methods for this task. Our entry placed 10th overall, but we were particularly proud to achieve a 3rd place score in the readability evaluation metric.

## introduction the biolaysumm 2023 task 1: lay summarization of biomedical articles @xcite is to generate lay summaries for two biomedical datasets @xcite , plos and elife, respectively. the aim of this task is to generate simplified and easy-to-understand summaries of the original articles that can be readily comprehended by the general public. figure 1 provides an information of this task dataset, showcasing examples that highlight the importance of not only generating abstracts for the text but also simplifying the content. in the examples, the bold vocabulary in the article text represents biomedical terminology that may be difficult for the general public to understand. therefore, it is crucial to transform such terminology into language that is more accessible and easily understood by a lay audience, as depicted by the bold vocabulary in the lay summary. this transformation plays a vital role in ensuring that the generated lay summaries are both comprehensible and effectively convey the key information to non-expert readers. the average length of articles in the elife dataset is 8,442 words, while for the plos dataset, it is 5,864 words. @xcite . using a state-of-the-art article: the virus sars-cov-2 can exploit biological vulnerabilities (e.g. host proteins) in susceptible hosts that predispose to the development of severe covid-19. to identify host proteins that may contribute to the risk of severe covid-19, we undertook proteome-wide genetic colocalisation tests, ... ## conclusion overall, fine-tuning the pre-trained led model for lay summary generation tasks on the plos and elife datasets produced satisfactory results. surprisingly, the model demonstrated excellent scores on readability metrics, indicating its effectiveness in text simplification. however, the use of information design prompts such as keywords and headings did not improve the led model's ability to learn and generate better lay summaries as expected, and most of the evaluation metrics declined. the final results of our experiments have revealed the need for further investigation and research into the prompt strategy that was initially intended to enhance the generation of lay summaries. we aim to determine the underlying causes of the decline observed in the evaluation metrics. possible factors contributing to this decline include inadequate quality or insufficient differentiation of keywords from the content or abstract, excessive links or correlations within the keywords, or inconsistencies in the formatting of headings across articles, which may hinder the model's ability to grasp the importance of each paragraph's content. to address these issues and improve the effectiveness of the prompts, we will explore and develop new methods in future research.