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Automatic Summarization of Conversational Multi-Party Speech

Document summarization has proven to be a desirable component in many information management systems, complementing core information retrieval and browsing functionalities. The use of document summarization techniques is especially important with speech documents such as meeting transcriptions, since they are particularly difficult to navigate. As opposed to their written counterparts, spoken documents lack structural elements like title, headers, and paragraphs that can ease the task of the information seeker. Document summarization can reduce the overhead of navigating large collections of speech data. For example, summarization techniques can be used to extract or highlight relevant passages in a full transcription. Alternatively, similar techniques might be used to generate short text substitutes, or abstracts, that capture the “aboutness” of the meeting, while discarding disfluencies and other unimportant elements. In my thesis work, I address the problem of creating abstractive summaries of meetings, a task that has to date only received limited attention. Abstractive summarization differs from extraction in that it does not simply concatenate input utterances, but alters the extracted material in order to produce fluent and concise summaries. Speech summarization faces many challenges not found in texts, in particular high word error rates (WER on this data is 34.8%), absence of punctuation, and sometimes lack grammaticality and coherent ordering. My approach to speech summarization divides the problem into two subtasks that are admittedly quite independent: content selection, i.e. identifying a set of salient utterances that is a practical substitute to the entire meeting transcription; utterance revision, i.e. correcting the various non-fluencies typical of conversational speech, and operating below the sentence level to further remove unimportant lexical material.