Transformer-Based Abstractive Summarization for Depression Detection Literature for Enhanced Medical Insights

Transformer-Based Abstractive Summarization for Depression Detection Literature for Enhanced Medical Insights | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 9 January 2025 V1 Latest version Share on Transformer-Based Abstractive Summarization for Depression Detection Literature for Enhanced Medical Insights Authors : Akshi Kumar 0000-0003-4263-7168 [email protected] , Aditi Sharma , and Saurabh Raj Sangwan Authors Info & Affiliations https://doi.org/10.22541/au.173641568.89918303/v1 311 views 180 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract The overwhelming surge in depression detection research presents significant challenges for mental health professionals and researchers in keeping pace with new advancements. This issue is particularly critical as timely access to insights from recent studies is essential for effective diagnosis and intervention strategies. Manually summarizing the growing body of literature is labour-intensive and prone to inconsistencies, creating an urgent need for automated summarization tools. This study introduces DepressiLex, a specialized corpus comprising 40 research papers from 2023-2024 focused on depression detection. Using transformer-based models like including Pre-training with Extracted Gap-Sentences for Abstractive Summarization (PEGASUS), Bidirectional and Auto-Regressive Transformers (BART), the Text-to-Text Transfer Transformer (T5-Base), the Longformer-Encoder-Decoder (LED), and ProphetNet, to evaluate their effectiveness in generating abstractive summaries. We assessed their performance using metrics such as the Bilingual Evaluation Understudy (BLEU) and the Recall-Oriented Understudy for Gisting Evaluation (ROGUE), with the Longformer-Encoder-Decoder consistently outperforming the others. This engineering advancement fulfils a critical need in healthcare, providing mental health professionals with streamlined, artificial intelligence (AI)-enabled access to key insights, thereby significantly reducing the time and cognitive load involved in reviewing complex research. Additionally, word cloud visualizations highlight the dominant themes and terms across the summaries, underscoring the potential of transformer models to transform access to mental health knowledge. Supplementary Material File (manuscript_ast.docx) Download 2.11 MB Information & Authors Information Version history V1 Version 1 09 January 2025 Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords abstractive summarization artificial intelligence in healthcare depression detection literature medical text summarization transformer models Authors Affiliations Akshi Kumar 0000-0003-4263-7168 [email protected] Goldsmiths University of London Department of Computing View all articles by this author Aditi Sharma Thapar Institute of Engineering and Technology (Deemed to be University) View all articles by this author Saurabh Raj Sangwan Galgotias University School of Computing Science and Engineering View all articles by this author Metrics & Citations Metrics Article Usage 311 views 180 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Akshi Kumar, Aditi Sharma, Saurabh Raj Sangwan. Transformer-Based Abstractive Summarization for Depression Detection Literature for Enhanced Medical Insights. Authorea . 09 January 2025. DOI: https://doi.org/10.22541/au.173641568.89918303/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu . Format Please select one from the list RIS (ProCite, Reference Manager) EndNote BibTex Medlars RefWorks Direct import Tips for downloading citations document.getElementById('citMgrHelpLink').addEventListener('click', function() { popupHelp(this.href); return false; }); $(".js__slcInclude").on("change", function(e){ if ($(this).val() == 'refworks') $('#direct').prop("checked", false); $('#direct').prop("disabled", ($(this).val() == 'refworks')); }); Cited by Akash Ghosh, Raghav Jain, Anubhav Jhangra, Sriparna Saha, Adam Jatowt, A Survey on Medical Document Summarization: From Machine Learning Techniques to Large Language Models, WIREs Data Mining and Knowledge Discovery, 15 , 4, (2025). https://doi.org/10.1002/widm.70045 Crossref Loading... View Options View options PDF View PDF Figures Tables Media Share Share Share article link Copy Link Copied! Copying failed. 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