Pokhrel, Lilakant, Pokhrel, Sangita ORCID: https://orcid.org/0009-0008-2092-7029, Ganesan, Swathi ORCID: https://orcid.org/0000-0002-6278-2090 and Somasiri, Nalinda ORCID: https://orcid.org/0000-0001-6311-2251 (2026) A Comparative Study of Hybrid Text Summarization Techniques Using Traditional, Static, and Contextual Word Embeddings. Artificial Intelligence and Applications.

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Abstract

Currently, there is an exponential growth in textual data due to the rapid expansion of internet technology and various social and entertainment platforms. This is causing widespread negative effects on document and text management, text classification, and information retrieval, among other areas. To address the challenge of translating large volumes of text into natural-sounding language that retains citations, quotes, and references, summarization research is gaining significant international attention. The focus is on developing an effective hybrid (extractive and abstractive) summarization system by implementing different word embedding and summarization techniques. The study examines various embedding methods, from traditional approaches (Count Vector, TF-IDF) to static word embeddings (Word2Vec, GloVe, FastText), and contextualized embeddings (ELMo, GPT, BERT). It employs efficient extractive methods (TF-IDF, Textrank, LSA, Word2Vec, ELMo with K-means) and abstractive methods (Bi-LSTM, GPT) to create an effective summarization system. By integrating eight summarization techniques into Django-based (Python) web applications, the research finds that GPT performs particularly well, followed by TextRank and Elmo-based systems, with their ROUGE scores compared. The study also discusses the challenges faced in developing such a system and outlines future directions for ongoing research.

Item Type:	Article
Status:	Published
DOI:	10.47852/bonviewAIA62026330
Subjects:	T Technology > T Technology (General)
School/Department:	London Campus
URI:	https://ray.yorksj.ac.uk/id/eprint/15341

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