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Title: TL;DR: Automatic Summarization With Textual Annotations
Authors: Griggs, Julian
Advisors: Gunawardena, Ananda
Department: Computer Science
Class Year: 2015
Abstract: Automatic summarization is a powerful means for compressing large quantities of text into manageable chunks for human consumption. Despite the growth in blogs and other platforms that facilitate human interaction with text, there have been relatively few studies aimed at incorporating the auxiliary annotation data provided by these platforms into the summarization task. In this paper, I introduce a suite of summarization algorithms that utilize textual annotations (highlights and comments) to effectively summarize text. Specifically, two of the systems developed, ATMS-A (Hdp) and the Hotspot Summarization are shown to outperform all competitive baseline systems.
Extent: 60 pages
Type of Material: Princeton University Senior Theses
Language: en_US
Appears in Collections:Computer Science, 1988-2017

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