Automatic minute generation for parliamentary speech using conditional random fields

We show a novel approach of automatically generating minutes style extractive summaries for parliamentary speech. Minutes are structured summaries consisting of sequences of business items with sub-summaries. We propose to model minute structures as a rhetorical syntax tree. We also propose to use a single Conditional Random Field classifier to carry out the chunking and parsing of a parliamentary speech according to this syntax tree, and extracting salient sentences, all in one step, to form a meeting minute automatically. We show that this one step minute generation system outperforms a more traditional two step system where a first classifier is used for chunking and parsing and a second classifier is used for sentence extraction, from 69.5% to 73.2% in ROUGE-L measure. We also show comparative results from different features in the classifier and found that acoustic features contribute similarly to the final performance as N-gram features from ASR output.