Text segmentation of spoken meeting transcripts

Abstract

Text segmentation has played an important role in information retrieval as well as natural language processing. Current segmentation methods are well suited for written and structured texts making use of their distinctive macrolevel structures; however text segmentation of transcribed multi-party conversation presents a different challenge given its ill-formed sentences and the lack of macro-level text units. This paper describes an algorithm suitable for segmenting spoken meeting transcripts combining semantically complex lexical relations with speech cue phrases to build lexical chains in determining topic boundaries.

Text Mining in Big Data Analytics

“…Sharp and Chibelushi [230] presented an algorithm for the text classification of meeting transcripts. This study focused on the analysis of spoken meeting transcripts of discussions on software development, with the aim to determine the topics discussed in these meetings and, thereby, extract the decisions made and issues discussed.…”