Chương 1: Giới thiệu, • Chương 2: Lý thuyết tổng quan và các nghiên cứu gần đây, • Chương 3: Mô hình lai PSO-GA đề xuất, • Chương 4: Thực nghiệm, • Chương 5: Kết luận và nghiên cứu tương lai. Ngày tháng năm 2021 Giáo viên hướng dẫn Tác giả luận văn TS. Nguyễn Thị Thu Trang Nguyễn Thị Hoài CHỦ TỊCH HỘI ĐỒNG luan van hay luan van tot nghiep do an to nghiep docx 123docz luan van hay luan van tot nghiep do an to nghiep docx 123docz luan van hay luan van tot nghiep do an to nghiep docx 123docz luan van hay luan van tot nghiep do an to nghiep docx 123docz luan van hay luan van tot nghiep do an to nghiep docx 123docz luan van hay luan van tot nghiep do an to nghiep docx 123docz luan van hay luan van tot nghiep do an to nghiep docx 123docz luan van hay luan van tot nghiep do an to nghiep docx 123docz luan van hay luan van tot nghiep do an to nghiep docx 123docz luan van hay luan van tot nghiep do an to nghiep docx 123docz luan van hay luan van tot nghiep do an to nghiep docx 123docz luan van hay luan van tot nghiep do an to nghiep docx 123docz luan van hay luan van tot nghiep do an to nghiep docx 123docz luan van hay luan van tot nghiep do an to nghiep docx 123docz luan van hay luan van tot nghiep do an to nghiep docx 123docz luan van hay luan van tot nghiep do an to nghiep docx 123docz luan van hay luan van tot nghiep Graduation Thesis Assignment Name: Nguyen Thi Hoai Phone: +84 384 830 357 Email: hoai.vn Class: 20BKHDL-E Affiliation: Hanoi University of Science and Technology I – Nguyen Thi Hoai - hereby warrants that the work and presentation in this thesis are performed by myself under the supervision of Dr. Nguyen Thi Thu Trang.
All results presented in this thesis are truthful and are not copied from any other work. All references in this thesis - including images, tables, figures, and quotes - are clearly and fully documented in the bibliography. I will take full responsibility for even one copy that violates school regulations. Hanoi, 25th November 2021 Author Nguyen Thi Hoai luan van hay luan van tot nghiep do an to nghiep docx 123docz luan van hay luan van tot nghiep do an to nghiep docx 123docz luan van hay luan van tot nghiep do an to nghiep docx 123docz luan van hay luan van tot nghiep do an to nghiep docx 123docz luan van hay luan van tot nghiep do an to nghiep docx 123docz luan van hay luan van tot nghiep do an to nghiep docx 123docz luan van hay luan van tot nghiep do an to nghiep docx 123docz luan van hay luan van tot nghiep do an to nghiep docx 123docz luan van hay luan van tot nghiep do an to nghiep docx 123docz luan van hay luan van tot nghiep do an to nghiep docx 123docz luan van hay luan van tot nghiep do an to nghiep docx 123docz luan van hay luan van tot nghiep do an to nghiep docx 123docz luan van hay luan van tot nghiep do an to nghiep docx 123docz luan van hay luan van tot nghiep do an to nghiep docx 123docz luan van hay luan van tot nghiep do an to nghiep docx 123docz luan van hay luan van tot nghiep do an to nghiep docx 123docz luan van hay luan van tot nghiep Acknowledgement It is a genuine pleasure to express my deep sense of thanks and gratitude to my advisor and guide, Dr.
Nguyen Thi Thu Trang. I am highly grateful because she managed and supported me in completing my thesis. Her unwavering enthusiasm for science kept me constantly engaged with my research, and her personal generosity helped make my time at HUST enjoyable. Furthermore, she has taught me the methodology to carry out the study and present the research works as clearly as possible.
It was a great privilege and honor to work and study under her guidance. Also, I would like to thank Dr. Bui Thi Mai Anh for her keen interest in me in every research. Her prompt inspirations, timely suggestions with kindness, motivation, and dynamism have enabled me to accomplish this task throughout my study period.
Most importantly, my sincere thanks and appreciation also go to my family for their constant source of loving, caring and inspiration. They are such vital parts of my life that I cannot imagine a life without them. Last but not least, I gratefully acknowledge my university and the people who have willingly helped me out with their abilities. I greatly appreciate the support received through the collaborative work undertaken with them.
luan van hay luan van tot nghiep do an to nghiep docx 123docz luan van hay luan van tot nghiep do an to nghiep docx 123docz luan van hay luan van tot nghiep do an to nghiep docx 123docz luan van hay luan van tot nghiep do an to nghiep docx 123docz luan van hay luan van tot nghiep do an to nghiep docx 123docz luan van hay luan van tot nghiep do an to nghiep docx 123docz luan van hay luan van tot nghiep do an to nghiep docx 123docz luan van hay luan van tot nghiep do an to nghiep docx 123docz luan van hay luan van tot nghiep do an to nghiep docx 123docz luan van hay luan van tot nghiep do an to nghiep docx 123docz luan van hay luan van tot nghiep do an to nghiep docx 123docz luan van hay luan van tot nghiep do an to nghiep docx 123docz luan van hay luan van tot nghiep do an to nghiep docx 123docz luan van hay luan van tot nghiep do an to nghiep docx 123docz luan van hay luan van tot nghiep do an to nghiep docx 123docz luan van hay luan van tot nghiep do an to nghiep docx 123docz luan van hay luan van tot nghiep Abstract The task of text summarization is to generate main ideas that cover the content of the whole text with the aim of shortening reading time. There are two approaches to summarizing, extractive and abstractive summarization. Numerous methods have been researched in this domain, in which using heuristic algorithms is a more effective and straightforward way than applying machine learning or deep learning. A genetic algorithm (GA) is a search heuristic that is inspired by Charles Darwin’s theory of natural evolution.
This algorithm reflects the process of natural selection where the fittest individuals are selected for reproduction in order to produce offspring of the next generation. However, using traditional GA alone may suffer from a weak local search capability and slow convergence speed. Another algorithm, Particle Swarm Optimization (PSO), is a population-based optimization technique inspired by the motion of bird flocks and schooling fish. The premature convergence of PSO is prevented by applying GA on a small population.
Besides, the local optimum phenomenon of PSO can also be avoided with GA. The goal of the thesis is to investigate the effectiveness of a hybrid method combining GA and PSO based attribute selection in improving the performance of classification algorithms solving the automatic text summarization task. To assess the effectiveness of the proposed algorithm, I have conducted the experimentation on three common datasets, DUC2001, DUC2002, which are typically used for extractive summarization, and CNN/Daily Mail. The experiment results have shown that the hybrid PSO-GA outperforms all the state-of-the-art works on all three ROUGE point metrics for these datasets.
The solution presented in this thesis was accepted at The 35th Pacific Asia Conference on Language, Information and Computation (PACLIC 35) in 2021. luan van hay luan van tot nghiep do an to nghiep docx 123docz luan van hay luan van tot nghiep do an to nghiep docx 123docz luan van hay luan van tot nghiep do an to nghiep docx 123docz luan van hay luan van tot nghiep do an to nghiep docx 123docz luan van hay luan van tot nghiep do an to nghiep docx 123docz luan van hay luan van tot nghiep do an to nghiep docx 123docz luan van hay luan van tot nghiep do an to nghiep docx 123docz luan van hay luan van tot nghiep do an to nghiep docx 123docz luan van hay luan van tot nghiep do an to nghiep docx 123docz luan van hay luan van tot nghiep do an to nghiep docx 123docz luan van hay luan van tot nghiep do an to nghiep docx 123docz luan van hay luan van tot nghiep do an to nghiep docx 123docz luan van hay luan van tot nghiep do an to nghiep docx 123docz luan van hay luan van tot nghiep do an to nghiep docx 123docz luan van hay luan van tot nghiep do an to nghiep docx 123docz luan van hay luan van tot nghiep do an to nghiep docx 123docz luan van hay luan van tot nghiep Content Abstract .ii List of Figures. v List of Tables. vi List of Equations.
viii Chapter 1 Introduction .2 Objective and scope .3 Structure of thesis. 2 Chapter 2 Theoretical Background and Related works .3 Particle swarm optimization.1 Particle and swarm .4 Term Frequency– Inverse Document Frequency .2 Inverse Document Frequency. 16 Chapter 3 Proposed hybrid PSO-GA. 18 ii luan van hay luan van tot nghiep do an to nghiep docx 123docz luan van hay luan van tot nghiep do an to nghiep docx 123docz luan van hay luan van tot nghiep do an to nghiep docx 123docz luan van hay luan van tot nghiep do an to nghiep docx 123docz luan van hay luan van tot nghiep do an to nghiep docx 123docz luan van hay luan van tot nghiep do an to nghiep docx 123docz luan van hay luan van tot nghiep do an to nghiep docx 123docz luan van hay luan van tot nghiep do an to nghiep docx 123docz luan van hay luan van tot nghiep do an to nghiep docx 123docz luan van hay luan van tot nghiep do an to nghiep docx 123docz luan van hay luan van tot nghiep do an to nghiep docx 123docz luan van hay luan van tot nghiep do an to nghiep docx 123docz luan van hay luan van tot nghiep do an to nghiep docx 123docz luan van hay luan van tot nghiep do an to nghiep docx 123docz luan van hay luan van tot nghiep do an to nghiep docx 123docz luan van hay luan van tot nghiep do an to nghiep docx 123docz luan van hay luan van tot nghiep 3.4 Proposed of fitness function.
21 a) Similarity to the topic sentence. 22 d) Number of proper nouns .5 Proposed strategies for operators .6 Adaptive PSO strategy .1 Precision, recall and f-score .2 Preparation for experiment .