Incorporating Lexical and Prosodic Information at Different Levels for Meeting Summarization

This paper investigates how prosodic features can be used to augment lexical features for meeting summarization. Automatic detection of summary-worthy content using non-lexical features, like prosody, has generally focused on features calculated over dialogue acts. However, a salient role of prosody is to distinguish important words within utterances. To examine whether including more fine grained prosodic information can help extractive summarization, we perform experiments incorporating lexical and prosodic features at different levels. For ICSI and AMI meeting corpora, we find that combining prosodic and lexical features at a lower level has better AUROC performance than adding in prosodic features derived over dialogue acts. ROUGE F-scores also show the same pattern for the ICSI data. However, the differences are less clear for the AMI data where the range of scores is much more compressed. In order to understand the relationship between the generated summaries and differences in standard measures, we look at the distribution of extracted content over meeting as well as summary redundancy. We find that summaries based on dialogue act level prosody better reflect the amount of human annotated summary content in meeting segments, while summaries derived from prosodically augmented lexical features exhibit less redundancy.