A Quantitative and Qualitative Assessment of Automatic Text Summarization Systems

Batista, Jamilson; Ferreira, Rodolfo; Tomaz, Hilário; Ferreira, Rafael; Lins, Rafael Dueire; Simske, Steven J.; Silva, Gabriel; Riss, Marcelo

doi:10.1145/2682571.2797081

Cited by 10 publications

(1 citation statement)

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“…In software engineering mostly extractive methods of summarization have been used. Text summarization is most often applied to large documents such as news reports [5] to allow for concise reading of the entire document. Researchers have been benchmarking and adopting summarization techniques to improve the accu-racy of summarizing software artifacts.…”

Section: Extractive Text Summarizationmentioning

confidence: 99%

ESSMArT way to manage customer requests

et al. 2019

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Quality and market acceptance of software products is strongly influenced by responsiveness to user requests. Once a request is received from a customer, decisions need to be made if the request should be escalated to the development team. Once escalated, the ticket must be formulated as a development task and be assigned to a developer. To make the process more efficient and reduce the time between receiving and escalating the user request, we aim to automate of the complete user request management process. We propose a holistic method called ESSMArT. The methods performs text summarization, predicts ticket escalation, creates the title and content of the ticket used by developers, and assigns the ticket to an available developer. We internally evaluated the method by 4,114 user tickets from Brightsquid and their secure health care communication platform Secure-Mail. We also perform an external evaluation on the usefulness of the approach. We found that supervised learning based on context specific data performs best for extractive summarization. For predicting escalation of tickets, Random Forest trained on a combination of conversation and extractive summarization is best with highest precision (of 0.9) and recall (of 0.55). From external evaluation we found that ESSMArT provides suggestions that are 71% aligned with human ones. Applying the prototype implementation to 315 user requests

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Section: Extractive Text Summarizationmentioning

confidence: 99%