DOI: https://doi.org/10.15368/theses.2020.77
Available at: https://digitalcommons.calpoly.edu/theses/2142
Date of Award
3-2020
Degree Name
MS in Computer Science
Department/Program
Computer Science
College
College of Engineering
Advisor
Christian Ekhardt
Advisor Department
Computer Science
Advisor College
College of Engineering
Abstract
In today’s world, we are always trying to find new ways to advance. This era has given rise to a global, distributed workforce since technology has allowed people to access and communicate with individuals all over the world. With the rise of remote workers, the need for quality communication tools has risen significantly. These communication tools come in many forms, and web-conference apps are among the most prominent for the task. Developing a system to automatically summarize the web-conference will save companies time and money, leading to more efficient meetings. Current approaches to summarizing multi-speaker web-conferences tend to yield poor or incoherent results, since conversations do not flow in the same manner that monologues or well-structured articles do. This thesis proposes a system of flags used to extract information from sentences, where the flags are fed into Machine Learning models to determine the importance of the the sentence with which they are associated. The system of flags shows promise for multi-speaker conference summaries.