2 options
Towards a practically useful text simplification system / Reno Kriz.
- Format:
- Book
- Thesis/Dissertation
- Author/Creator:
- Kriz, Reno, author.
- Language:
- English
- Subjects (All):
- Artificial intelligence.
- Computer and Information Science--Penn dissertations.
- Penn dissertations--Computer and Information Science.
- Local Subjects:
- Artificial intelligence.
- Computer and Information Science--Penn dissertations.
- Penn dissertations--Computer and Information Science.
- Genre:
- Academic theses.
- Physical Description:
- 1 online resource (158 pages)
- Contained In:
- Dissertations Abstracts International 83-03B.
- Place of Publication:
- [Philadelphia, Pennsylvania] : University of Pennsylvania ; Ann Arbor : ProQuest Dissertations & Theses, 2021.
- Language Note:
- English
- System Details:
- Mode of access: World Wide Web.
- text file
- Summary:
- While there is a vast amount of text written about nearly any topic, this is often difficult for someone unfamiliar with a specific field to understand. Automated text simplification aims to reduce the complexity of a document, making it more comprehensible to a broader audience. Much of the research in this field has traditionally focused on simplification sub-tasks, such as lexical, syntactic, or sentence-level simplification. However, current systems struggle to consistently produce high-quality simplifications. Phrase-based models tend to make too many poor transformations; on the other hand, recent neural models, while producing grammatical output, often do not make all needed changes to the original text. In this thesis, I discuss novel approaches for improving lexical and sentence-level simplification systems. Regarding sentence simplification models, after noting that encouraging diversity at inference time leads to significant improvements, I take a closer look at the idea of diversity and perform an exhaustive comparison of diverse decoding techniques on other generation tasks. I also discuss the limitations in the framing of current simplification tasks, which prevent these models from yet being practically useful. Thus, I also propose a retrieval-based reformulation of the problem. Specifically, starting with a document, I identify concepts critical to understanding its content, and then retrieve documents relevant for each concept, re-ranking them based on the desired complexity level.
- Notes:
- Source: Dissertations Abstracts International, Volume: 83-03, Section: B.
- Advisors: Callison-Burch, Chris; Apidianaki, Marianna; Committee members: Marcus, Mitch; Roth, Dan; Ungar, Lyle; Miltsakaki, Eleni.
- Department: Computer and Information Science.
- Ph.D. University of Pennsylvania 2021.
- Local Notes:
- School code: 0175
- ISBN:
- 9798535570952
- Access Restriction:
- Restricted for use by site license.
- This item must not be sold to any third party vendors.
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