Machine Learning, Text Summarization, and Optimizing Scholarship for Citizen Audiences and Discovery
Channel: CNI: Coalition for Networked Information
Date: 14/04/2022

Jason A. Clark, Montana State University
Leila Sterman, Montana State University
Daniel Laden, Montana State University

Open access allows digital/physical access to scholarship, but the story of scholarship and the accessibility of research concepts and ideas remains out of reach for some non-disciplinary-expert audiences. In turn, as the scholarly profile has emerged as a digital resume, marketing tool, and ledger, librarians have found a role as promoters and guides in the process of setting up, maintaining, disambiguating, and translating research for diverse audiences. In recognizing our translational roles in research communication, our project team worked to build readable, accessible scholarship for public understanding and for the facilitation of interdisciplinary scholarship and partnerships. The first part of this work involved surveying and learning about summarization practices of academics and discerning how to perhaps create a “snapshot” of a scholarly article (a concise and readable summarization of the thesis, methods, findings, and major figures of an article) for a broad reading audience. We collected information regarding how or if authors currently seek to provide access to their research, their goals in doing so, and any barriers that they faced in the process. This survey data provided a foundation for the development of two new deliverables: a text summarization procedure for automating the snapshot article, and an implementation of the markup and structured data to encode the snapshot article for machine-readability and harvesting.

This session will report on survey findings about how authors promote and summarize their research, demonstrate how these findings inform the creation of snapshot articles for humanities and STEM audiences, and share how machine learning processes for abstractive summarization aid in access to scholarship for citizens and machines.

https://github.com/msulibrary/msu-article-summarization
 
Learn more: https://www.cni.org/topics/assessment/machine-learning-text-summarization-and-optimizing-scholarship-for-citizen-audiences-and-discovery
 
Coalition for Networked Information (CNI)
Presented at Spring 2022 Membership Meeting
Virtual: March 21-22, 2022
In-Person: March 28-29, 2022
 
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