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TL;DR: Automatic Summarization With Textual Annotations

datacite.rightsrestricted
dc.contributor.advisorGunawardena, Ananda
dc.contributor.authorGriggs, Julian
dc.date.accessioned2015-06-26T14:00:36Z
dc.date.accessioned2026-09-29T21:54:50Z
dc.date.available2015-06-26T14:00:36Z
dc.date.available2026-09-29T21:54:50Z
dc.date.created2015-04-30
dc.date.issued2015-06-26
dc.description.abstractAutomatic 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.en_US
dc.format.extent60 pagesen_US
dc.identifier.urihttp://arks.princeton.edu/ark:/88435/dsp01n870zt14x
dc.identifier.urihttps://theses-dissertations.princeton.edu/handle/88435/dsp01n870zt14x
dc.language.isoen_USen_US
dc.titleTL;DR: Automatic Summarization With Textual Annotationsen_US
dc.typePrinceton University Senior Theses
dspace.entity.typePublication
pu.date.classyear2015en_US
pu.departmentComputer Scienceen_US
pu.pdf.coverpageSeniorThesisCoverPage

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