AUTH at B io L ay S umm 2024: Bringing Scientific Content to Kids (2024)

Abstract: The BioLaySumm 2024 shared task at the ACL 2024 BioNLP workshop aims to transform biomedical research articles into lay summaries suitable for a broad audience, including children. We utilize the BioBART model, designed for the biomedical sector, to convert complex scientific data into clear, concise summaries. Our dataset, which includes a range of scientific abstracts, enables us to address the diverse information needs of our audience. This focus ensures that our summaries are accessible to both general and younger lay audience. Additionally, we employ specialized tokens and augmentation techniques to optimize the model’s performance. Our methodology proved effective, earning us the 7th rank on the final leaderboard out of 57 participants.

## introduction lay summarization (i.e. summarization for nonexpert audiences) helps make scientific literature understandable to non-experts. it simplifies complex technical information into clear, easy-tounderstand language, promoting public understanding of research findings. the significance of lay summarization, which bridges the gap between scientific insights and public knowledge, has been increasingly recognized @xcite . to address the challenge of dense technical language in biomedical research papers, the biolay-summ 2024 shared task @xcite at the acl 2024 bionlp workshop focuses on turning biomedical research into lay summaries. these summaries need to be accurate and understandable to a broad audience, since they serve an important role in informing the public about scientific developments and avoiding the spread of misinformation. the shared task is based on data from two sources of biomedical articles: elife and plos @xcite . our approach to this shared task is based on the biobart-v2 model @xcite , which has been demonstrated to be highly effective in summarizing biomedical content. on top of it, we employ a controllable generation technique using special tokens, in order to exploit in a single model the data from both elife and plos and at the same time during inference align the produced summaries with the unique characteristics of the corresponding source, such as length, readability and level of abstraction. in addition, towards improving the simplicity of the produced summaries, we employed augmentation to extend the elife and plos data. we identified the most complex lay summaries in these datasets, and paired their source abstracts with summaries produced by . to make it produce simple lay summaries, we used in-context learning providing to it examples of scientific articles targeted at children from the science journal for kids. ## the sjk dataset science journal for kids (sjk) is a non-profit organization based in texas that is dedicated to presenting scientific research in a manner that is accessible and appealing to children. they achieve this goal by digitally publishing on their web site foot_0 , adaptations of scientific papers that are made to be kid-friendly. the adaptation process undertaken by sjk involves using common vocabulary and relatable examples, and then further validating and refining the adapted content for educational use. this process ensures that the content is not only accessible but also retains the educational value of the original scientific research. the kid-friendly articles in the sjk web site are available in pdf format. to assemble the sjk dataset, we collected the pdfs of the articles and extracted their content. from this content we kept the abstracts and the links at the references section, pointing to the original scientific papers. we extracted doi numbers from these links and attempted to retrieve the abstracts via the semantic scholar api. however, since the api often returned empty abstracts, we resorted also to an extensive scraping process on specific pages with a particular format to get the abstracts. this was not always feasible due to restrictions on scraping from certain sites, necessitating manual addition of links and abstracts to the dataset, highlighting the challenging nature of the data collection process. we crawled the sjk web site on january 22, 2024. we initially collected all 306 articles from the sjk web site for potential future work, ensuring we had a comprehensive dataset to expand or refine as needed. from these, we eventually selected 285 articles based on their formatting suitability. older versions had a completely different format that the scraping process couldn't recognize because it was based on the newer versions. formatting issues included instances where the text from the abstract was cut off when scraped due to the two-column format or where scraping couldn't find the reference. we manually conducted additional checks to append missing text and locate references in these cases. additionally, we prioritized articles that included references. besides ensuring the credibility of the content, this allowed us to pair the kid-friendly articles with the corresponding scientific articles that inspired them. our final dataset foot_1 comprises 300 pairs, each consisting of an abstract from a scientific paper and its corresponding abstract from the children's article. we focused on abstracts because they provided comprehensive information suitable for our lay summarization task. for each article, we sourced the corresponding abstract from the first reference cited in the children's articles published by sjk, and in 25 cases, also the second reference, which are the original academic papers that the sjk articles are based on. the articles were intentionally curated to encompass a wide array of subjects, specifically chosen to attract the scientific curiosity of young learners across disciplines such as biology, chemistry, and more. ## results and discussion this section presents and discuss the results on the validation datasets provided by elife and plos. ## conclusion our approach to the biolaysumm 2024 shared task showcases biobart's ability to simplify complex biomedical research articles into accessible lay summaries. by fine-tuning biobart with specialized tokens and data augmentation techniques, we generated readable summaries for specific audiences, including younger readers. a key aspect of our methodology was the use of specialized tokens to precisely control the characteristics of each dataset and audience. additionally, we enriched our dataset with kid-friendly content from the science journal for kids, enabling us to produce summaries that effectively bridge the gap between scientific complexity and public understanding. our experimental results highlight the effectiveness of our approach, especially in improving the readability and relevance of the summaries. while our methodology significantly improved readability and relevance, maintaining factual accuracy remains a challenge. ensuring the factuality of lay summaries is especially critical in the biomedical field, where accuracy is important. our model achieved an 7th place out of 55 participants, demonstrating its validity in managing diverse and complex summarization tasks. this achievement shows the potential of our techniques in making scientific knowledge more accessible to the general public and children.