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研究生：	唐科南 Thompson, Keenan Nathaniel
論文名稱：	Diversity and Quality: Comparing Decoding Methods with PEGASUS for Text Summarization Diversity and Quality: Comparing Decoding Methods with PEGASUS for Text Summarization
指導教授：	陳柏琳 Chen, Berlin
口試委員：	陳冠宇 Chen, Kuan-Yu 陳柏琳 Chen, Berlin 劉士弘 Liu, Shi-Hung
口試日期：	2021/10/24
學位類別：	碩士 Master
系所名稱：	資訊工程學系 Department of Computer Science and Information Engineering
論文出版年：	2021
畢業學年度：	109
語文別：	英文
論文頁數：	35
英文關鍵詞：	summarization, diverse decoding, PEGASUS, ROUGE, lexical diversity
研究方法:	實驗設計法
DOI URL：	http://doi.org/10.6345/NTNU202101759
論文種類：	學術論文
相關次數：	點閱：87 下載：4
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This thesis offers three major contributions: (1) It considers a number of diverse decoding methods to address degenerate repetition in model output text and investigates what can be done to mitigate the loss in summary quality associated with the use of such methods. (2) It provides evidence that measure of textual lexical diversity (MTLD) is as viable tool as perplexity is for comparing text diversity in this context. (3) It presents a detailed analysis of the strengths and shortcomings of ROUGE, particularly in regard to abstractive summarization. To explore these issues the work analyzes the results of experiments run on the CNN/DailyMail dataset with the PEGASUS model.

Abstract	2
Table of Contents	3
Introduction	5
1 Background and Motivation	5
Related Works	7
1 Approaches and Models	7
1.1 Extractive, Abstractive, and Hybrid Approaches	7
1.2 PEGASUS	9
1.2.1 Diagram | PEGASUS Architecture	10
2 Metrics	11
2.1 Summary Quality	11
2.1.1 Formula | ROUGE-N	12
2.1.2 Formula | ROUGE-L	12
2.2 Lexical Diversity	15
3 Neural Text Degeneration	16
3.1 Formula | Top-k	18
3.2 Formula | Temperature	18
3.3 Formula | Nucleus Sampling	19
Methodology	20
1 Environment	20
2 Metrics	20
2.1 ROUGE	20
2.2 MTLD	20
3 Dataset	21
4 Models	21
4.1 PEGASUS	21
5 Experiments	22
5.1 Baselines	22
5.2 Diverse Decoding Strategies	22
Results and Discussion	24
1 Table | Large | All	24
2 Table | Fine-tuned | All	25
3 Table | PEGASUS Paper Results	26
4 Sample | Fine-tuned | k = 40	27
5 Table | Fine-tuned | Nucleus Sampling	28
6 Sample | Fine-tuned | k = 640, k = 640 and t = 0.7	30
7 Sample | Fine-tuned | p = 0.80, 0.85, 0.90, 0.95	31
Conclusion	33
Bibliography	34
                                

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