Automatic meeting summarization and topic detection system

Purpose: Producing meeting documents requires an instantaneous recorder during meetings, which costs extra human resources and takes time to amend the file. However, a high-quality meeting document can enable users to recall the meeting content efficiently. The paper aims to discuss these issues. Design/methodology/approach: An application based on this framework is developed to help the users find topics and obtain summarizations of meeting contents without extra effort. This app uses the Bluemix speech recognizer to obtain speech transcripts. It then combines latent Dirichlet allocation and a TextTiling algorithm with the speech script of meetings to detect boundaries between different topics and evaluate the topics in each segment. TextTeaser, an open API based on a feature-based approach, is then used to summarize the speech transcripts. Findings: The results indicate that the summaries generated by the machine are 85 percent similar to the records written by humankind. Originality/value: To reduce the human effort in generating meeting reports, this paper presents a framework to record and analyze meeting contents automatically by voice recognition, topic detection, and extractive summarization.