  The rapid growth of text data has motivated the development of
machine-learning based automatic text summarization strategies that concisely
capture the essential ideas in a larger text. This study aimed to devise an
extractive summarization method for A-133 Single Audits, which assess if
recipients of federal grants are compliant with program requirements for use of
federal funding. Currently, these voluminous audits must be manually analyzed
by officials for oversight, risk management, and prioritization purposes.
Automated summarization has the potential to streamline these processes.
Analysis focused on the "Findings" section of ~20,000 Single Audits spanning
2016-2018. Following text preprocessing and GloVe embedding, sentence-level
k-means clustering was performed to partition sentences by topic and to
establish the importance of each sentence. For each audit, key summary
sentences were extracted by proximity to cluster centroids. Summaries were
judged by non-expert human evaluation and compared to human-generated summaries
using the ROUGE metric. Though the goal was to fully automate summarization of
A-133 audits, human input was required at various stages due to large
variability in audit writing style, content, and context. Examples of human
inputs include the number of clusters, the choice to keep or discard certain
clusters based on their content relevance, and the definition of a top
sentence. Overall, this approach made progress towards automated extractive
summaries of A-133 audits, with future work to focus on full automation and
improving summary consistency. This work highlights the inherent difficulty and
subjective nature of automated summarization in a real-world application.
