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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：108 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.
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