Date of Award


Degree Name

MS in Computer Science


Computer Science


Alexander Dekhtyar


Today’s online content increases at an alarmingly rate which exceeds users’ ability to consume such content. Modern search techniques allow users to enter keyword queries to find content they wish to see. However, such techniques break down when users freely browse the internet without knowing exactly what they want. Users may have to invest an unnecessarily long time reading content to see if they are interested in it. Automatic text summarization helps relieve this problem by creating synopses that significantly reduce the text while preserving the key points. Steffen Lyngbaek created the SPORK summarization pipeline to solve the content overload in Reddit comment threads. Lyngbaek adapted the Opinosis graph model for extractive summarization and combined it with agglomerative hierarchical clustering and the Smith-Waterman algorithm to perform multi-document summarization on Reddit comments.

This thesis presents WHISK as a pipeline for general multi-document text summarization based on SPORK. A generic data model in WHISK allows creating new drivers for different platforms to work with the pipeline. In addition to the existing Opinosis graph model adapted in SPORK, WHISK introduces two simplified graph models for the pipeline. The simplified models removes unnecessary restrictions inherited from Opinosis graph’s abstractive summarization origins. Performance measurements and a study with Digital Democracy compare the two new graph models against the Opinosis graph model. Additionally, the study evaluates WHISK’s ability to generate pull quotes from political discussions as summaries.