EXPLORING AUTOMATED SUMMARIZATION: FROM EXTRACTION TO ABSTRACTION
This paper provides a review of AI-powered automated summarization models, with a focus on two principal approaches: extractive and abstractive. The study aims to evaluate the capabilities of these models in generating concise yet meaningful summaries and analyze their lexical proficiency and lingu...
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Volgograd State University
2024-11-01
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| Series: | Vestnik Volgogradskogo Gosudarstvennogo Universiteta. Seriâ 2. Âzykoznanie |
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| Online Access: | https://l.jvolsu.com/index.php/en/archive-en/928-science-journal-of-volsu-linguistics-2024-vol-23-no-5/artificial-intelligence-potential-in-natural-language-processing-and-machine-translation/2842-sorokina-s-g-exploring-automated-summarization-from-extraction-to-abstraction |
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| author | Svetlana G. Sorokina |
| author_facet | Svetlana G. Sorokina |
| author_sort | Svetlana G. Sorokina |
| collection | DOAJ |
| description | This paper provides a review of AI-powered automated summarization models, with a focus on two principal approaches: extractive and abstractive. The study aims to evaluate the capabilities of these models in
generating concise yet meaningful summaries and analyze their lexical proficiency and linguistic fluidity. The compression rates are assessed using quantitative metrics such as page, word, and character counts, while language fluency is described in terms of ability to manipulate grammar and lexical patterns without compromising meaning and content. The study draws on a selection of scientific publications across various disciplines, testing the functionality and output quality of automated summarization tools such as Summate.it, WordTune, SciSummary, Scholarcy, and OpenAI ChatGPT-4. The findings reveal that the selected models employ a hybrid strategy, integrating both extractive and abstractive techniques. Summaries produced by these tools exhibited varying degrees of completeness and accuracy, with page compression rates ranging from 50 to 95%, and character count reductions reaching up to 98%. Qualitative evaluation indicated that while the models generally captured the main ideas of the
source texts, some summaries suffered from oversimplification or misplaced emphasis. Despite these limitations, automated summarization models exhibit significant potential as effective tools for both text compression and content generation, highlighting the need for continued research, particularly from the perspective of linguistic analysis.
Summaries generated by AI models offer new opportunities for analyzing machine-generated language and provide valuable data for studying how algorithms process, condense, and restructure human language. |
| format | Article |
| id | doaj-art-710158571d29437c948abc00d24bddf5 |
| institution | DOAJ |
| issn | 1998-9911 2409-1979 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Volgograd State University |
| record_format | Article |
| series | Vestnik Volgogradskogo Gosudarstvennogo Universiteta. Seriâ 2. Âzykoznanie |
| spelling | doaj-art-710158571d29437c948abc00d24bddf52025-08-20T02:40:20ZengVolgograd State UniversityVestnik Volgogradskogo Gosudarstvennogo Universiteta. Seriâ 2. Âzykoznanie1998-99112409-19792024-11-01235475910.15688/jvolsu2.2024.5.4EXPLORING AUTOMATED SUMMARIZATION: FROM EXTRACTION TO ABSTRACTIONSvetlana G. Sorokina0https://orcid.org/0000-0002-8667-6743I.M. Sechenov First Moscow State Medical University, Moscow, RussiaThis paper provides a review of AI-powered automated summarization models, with a focus on two principal approaches: extractive and abstractive. The study aims to evaluate the capabilities of these models in generating concise yet meaningful summaries and analyze their lexical proficiency and linguistic fluidity. The compression rates are assessed using quantitative metrics such as page, word, and character counts, while language fluency is described in terms of ability to manipulate grammar and lexical patterns without compromising meaning and content. The study draws on a selection of scientific publications across various disciplines, testing the functionality and output quality of automated summarization tools such as Summate.it, WordTune, SciSummary, Scholarcy, and OpenAI ChatGPT-4. The findings reveal that the selected models employ a hybrid strategy, integrating both extractive and abstractive techniques. Summaries produced by these tools exhibited varying degrees of completeness and accuracy, with page compression rates ranging from 50 to 95%, and character count reductions reaching up to 98%. Qualitative evaluation indicated that while the models generally captured the main ideas of the source texts, some summaries suffered from oversimplification or misplaced emphasis. Despite these limitations, automated summarization models exhibit significant potential as effective tools for both text compression and content generation, highlighting the need for continued research, particularly from the perspective of linguistic analysis. Summaries generated by AI models offer new opportunities for analyzing machine-generated language and provide valuable data for studying how algorithms process, condense, and restructure human language.https://l.jvolsu.com/index.php/en/archive-en/928-science-journal-of-volsu-linguistics-2024-vol-23-no-5/artificial-intelligence-potential-in-natural-language-processing-and-machine-translation/2842-sorokina-s-g-exploring-automated-summarization-from-extraction-to-abstractionautomated summarizationextractive summarizationabstractive summarizationartificial intelligenceneural networksinterdisciplinary research |
| spellingShingle | Svetlana G. Sorokina EXPLORING AUTOMATED SUMMARIZATION: FROM EXTRACTION TO ABSTRACTION Vestnik Volgogradskogo Gosudarstvennogo Universiteta. Seriâ 2. Âzykoznanie automated summarization extractive summarization abstractive summarization artificial intelligence neural networks interdisciplinary research |
| title | EXPLORING AUTOMATED SUMMARIZATION: FROM EXTRACTION TO ABSTRACTION |
| title_full | EXPLORING AUTOMATED SUMMARIZATION: FROM EXTRACTION TO ABSTRACTION |
| title_fullStr | EXPLORING AUTOMATED SUMMARIZATION: FROM EXTRACTION TO ABSTRACTION |
| title_full_unstemmed | EXPLORING AUTOMATED SUMMARIZATION: FROM EXTRACTION TO ABSTRACTION |
| title_short | EXPLORING AUTOMATED SUMMARIZATION: FROM EXTRACTION TO ABSTRACTION |
| title_sort | exploring automated summarization from extraction to abstraction |
| topic | automated summarization extractive summarization abstractive summarization artificial intelligence neural networks interdisciplinary research |
| url | https://l.jvolsu.com/index.php/en/archive-en/928-science-journal-of-volsu-linguistics-2024-vol-23-no-5/artificial-intelligence-potential-in-natural-language-processing-and-machine-translation/2842-sorokina-s-g-exploring-automated-summarization-from-extraction-to-abstraction |
| work_keys_str_mv | AT svetlanagsorokina exploringautomatedsummarizationfromextractiontoabstraction |