Publication Date
2023
Document Type
Thesis
Committee Members
Valerie Shalin, Ph.D. (Advisor); Sarah Bibyk, Ph.D. (Committee Member); David Lahuis, Ph.D. (Committee Member)
Degree Name
Master of Science (MS)
Abstract
In this work, I explore the development of computational methods for automatically creating after-action reports of JTAC radio conversations. Prior research has investigated related issues of sentence compression, text summarization, and conversation summarization (Banerjee, Mitra, & Sugiyama, 2015; Clarke & Lapata, 2008; L. Wang & Cardie, 2012; Raffel et al., 2020). However, this work makes limiting assumptions about what features are relevant to a summary and what sources of information should be included. I propose methods that combine knowledge from linguistic, procedural, and domain sources to address these limitations. Results indicate that the proposed model performs better than some of our baseline models, but highlight several challenges of automatically scoring computational summaries, particularly in technical low-resource domains.
Page Count
167
Department or Program
Department of Psychology
Year Degree Awarded
2023
Copyright
Copyright 2023, all rights reserved. My ETD will be available under the "Fair Use" terms of copyright law.
