000			02964nam a22003257a 4500
003			OSt
005			20250711094748.0
008			250711b |||||||| |||| 00| 0 eng d
040			_aTUPM _bEnglish _cTUPM _dTUPM _erda
050			_aBTH T 58.5 _bB37 2025
100			_aBarrios, Armand Angelo C. _eauthor
245			_aSummpy: _bdevelopment of an optimized summarization framework for thesis into structured imrad format utilizing the bart large language model and textrank algorithms for enhanced academic writing and research efficiency/ _cArmand Angelo C. Barrios, Sophia Mer C. Enriquez, Almira Jill O. Garcia, Janna Rose V. Herrera, and Andrew R. Oloroso.--
260			_aManila: _bTechnological University of the Philippines, _c2025.
300			_aix, 142pages: _c29cm.
336			_2rdacontent
337			_2rdamedia
338			_2rdacarrier
500			_aBachelor's thesis
502			_aCollege of Science.-- _bBachelor of science in computer science: _cTechnological University of the Philippines, _d2025.
504			_aIncludes bibliographic references and index.
520			_aThis study presents SummPy, a web-based application designed to generate structured summaries of academic theses using the IMRaD format that includes Introduction, Methods, Results, and Discussion. With the increasing volume and complexity of academic research, students and professionals often face challenges in efficiently digesting and summarizing lengthy documents. SummPy addresses this by leveraging advanced Natural Language Processing (NLP) techniques and Large Language Models (LLMs), specifically BART for abstractive summarization and TextRank for extractive summarization. The system processes the uploaded PDF files of the users, the system uses hierarchical multi-threading for efficiency, and evaluates the coherence and accuracy of generated summaries using DistilBERT for semantic similarity analysis. The project followed a systematic methodology involving document segmentation, parallel processing, summarization, and evaluation. Results demonstrate that SummPy effectively produces accurate, well-structured summaries that maintain the original research's context and key insights. The tool significantly reduces the time and effort required for manual summarization, offering a practical solution for academic and professional use. Evaluation metrics indicate high usability, security, and portability, affirming its adaptability across various environments. In conclusion, SummPy emerges as a valuable resource for enhancing academic productivity, understanding, and knowledge dissemination.
650			_aIMRAD format
650			_aAbstractive summarization
650			_aSemantic similarity analysis
700			_aEnriquez, Sophia Mer C. _eauthor
700			_aGarcia, Almira Jill O. _eauthor
700			_aHerrera, Janna Rose V. _eauthor
700			_aOloroso, Andrew R. _eauthor
942			_2lcc _cBTH COS _n0
999			_c30292 _d30292