Automatic Indexing of Lecture Presentations Using Unsupervised Learning of Presumed Discourse Markers

Abstract

A new method for automatic detection of section boundaries and extraction of key sentences from lecture audio archives is proposed. The method makes use of 'discourse markers' (DMs), which are characteristic expressions used in initial utterances of sections, together with pause and language model information. The DMs are derived in a totally unsupervised manner based on word statistics. An experimental evaluation using the Corpus of Spontaneous Japanese (CSJ) demonstrates that the proposed method provides better indexing of section boundaries compared with a simple baseline method using pause information only, and that it is robust against speech recognition errors. The method is also applied to extraction of key sentences that can index the section topics. The statistics of the presumed DMs are used to define the importance of sentences, which favors potentially section-initial ones. The measure is also combined with the conventional tf-idf measure based on content words. Experimental results confirm the effectiveness of using the DMs in combination with the keyword-based method. The paper also describes a statistical framework for transforming raw speech transcriptions into the document style for defining appropriate sentence units and improving readability.