Automated Text Summarization for the Enhancement of Public Services
Xingbang Liu, Janyl Jumadinova

TL;DR
This paper presents an AI-powered automated text summarization system that processes community interview data to identify key issues, saving significant human effort and aiding public service improvements in Meadville.
Contribution
The paper introduces a novel automated summarization system tailored for public sector community data, integrating keyword extraction, summarization, and search functionalities.
Findings
Saved over 300 hours of manual labor
Enabled efficient identification of community issues
Supported ongoing community projects
Abstract
Natural language processing and machine learning algorithms have been shown to be effective in a variety of applications. In this work, we contribute to the area of AI adoption in the public sector. We present an automated system that was used to process textual information, generate important keywords, and automatically summarize key elements of the Meadville community statements. We also describe the process of collaboration with My Meadville administrators during the development of our system. My Meadville, a community initiative, supported by the city of Meadville conducted a large number of interviews with the residents of Meadville during the community events and transcribed these interviews into textual data files. Their goal was to uncover the issues of importance to the Meadville residents in an attempt to enhance public services. Our AI system cleans and pre-processes the…
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Taxonomy
TopicsAdvanced Text Analysis Techniques · Topic Modeling · Text and Document Classification Technologies
