An Investigation in Generating Text summary that Adapts to a Specific Reader's Education Level
Dr. El Sayed Mahmoud
Date of Defense
Honours Bachelor of Computer Science (Mobile Computing)
natural language processing, Flesch Kincaid, machine learning, neural networks, summarization esis
Faculty of Applied Science & Technology (FAST)
Synthesizing a document summary that does not match the reader's education level is challenging and significantly reduces the knowledge uptake.
Synthesizing a document summary that does not match the reader's education level is challenging and significantly reduces the knowledge uptake. This work develops an automatic summary generator that adapts the generated summary to a reader's education level. Two summarization systems are developed. The first is a regular summarization system and the second incorporates readability metrics with the first system. The metrics facilitate altering the generated summary to match a specific reader education level. The two metrics: Flesch-Kincaid readability formula and New Dale Chall readability formula were used to avoid biases from each readability formula. The resulting summaries from the two systems are evaluated by a diverse group of readers who match the target education level. The readers complete a short survey that reflects the evaluation for the summaries. The resulting summaries are further evaluated using ROUGE scripts. The result of the surveys are analyzed to evaluate the benefit of incorporating education-level into automatically generated summaries.
© Harrison U-Ebili
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This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.
U-Ebili, Harrison, "An Investigation in Generating Text summary that Adapts to a Specific Reader's Education Level" (2020). Student Theses. 6.