{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T18:45:13Z","timestamp":1775069113737,"version":"3.50.1"},"reference-count":53,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2025,4,12]],"date-time":"2025-04-12T00:00:00Z","timestamp":1744416000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computation"],"abstract":"<jats:p>The field of text summarization has evolved from basic extractive methods that identify key sentences to sophisticated abstractive techniques that generate contextually meaningful summaries. In today\u2019s digital landscape, where an immense volume of textual data is produced every day, the need for concise and coherent summaries is more crucial than ever. However, summarizing short texts, particularly from platforms like Twitter, presents unique challenges due to character constraints, informal language, and noise from elements such as hashtags, mentions, and URLs. To overcome these challenges, this paper introduces a deep learning framework for automated short text summarization on Twitter. The proposed approach combines bidirectional encoder representations from transformers (BERT) with a transformer-based encoder\u2013decoder architecture (TEDA), incorporating an attention mechanism to improve contextual understanding. Additionally, long short-term memory (LSTM) networks are integrated within BERT to effectively capture long-range dependencies in tweets and their summaries. This hybrid model ensures that generated summaries remain informative, concise, and contextually relevant while minimizing redundancy. The performance of the proposed framework was assessed using three benchmark Twitter datasets\u2014Hagupit, SHShoot, and Hyderabad Blast\u2014with ROUGE scores serving as the evaluation metric. Experimental results demonstrate that the model surpasses existing approaches in accurately capturing key information from tweets. These findings underscore the framework\u2019s effectiveness in automated short text summarization, offering a robust solution for efficiently processing and summarizing large-scale social media content.<\/jats:p>","DOI":"10.3390\/computation13040096","type":"journal-article","created":{"date-parts":[[2025,4,15]],"date-time":"2025-04-15T08:11:39Z","timestamp":1744704699000},"page":"96","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Deep Learning-Based Short Text Summarization: An Integrated BERT and Transformer Encoder\u2013Decoder Approach"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5055-0137","authenticated-orcid":false,"given":"Fahd A.","family":"Ghanem","sequence":"first","affiliation":[{"name":"Department of Computer Science & Engineering, PES College of Engineering (Affiliated to University of Mysore), Mandya 571401, India"},{"name":"Department of Computer Science, College of Education-Zabid, Hodeidah University, Hodeidah P.O. Box 3114, Yemen"}]},{"given":"M. C.","family":"Padma","sequence":"additional","affiliation":[{"name":"Department of Computer Science & Engineering, PES College of Engineering (Affiliated to University of Mysore), Mandya 571401, India"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6631-051X","authenticated-orcid":false,"given":"Hudhaifa M.","family":"Abdulwahab","sequence":"additional","affiliation":[{"name":"Department of Computer Application, Ramaiah Institute of Technology (Affiliated to VTU), Bengaluru 560054, India"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3216-5048","authenticated-orcid":false,"given":"Ramez","family":"Alkhatib","sequence":"additional","affiliation":[{"name":"BMB Nord, Research Center Borstel, Parkallee 35, 23845 Borstel, Germany"}]}],"member":"1968","published-online":{"date-parts":[[2025,4,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Ghanem, F.A., Padma, M.C., and Alkhatib, R. (2023). 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