Single-Document and Multi-Document Summarization Techniques for Email Threads Using Sentence Compression First Author Affiliation / Address line 1

We present two approaches to email thread summarization: Collective Message Summarization (CMS) applies a multi-document summarization approach, while Individual Message Summarization (IMS) treats the problem as a sequence of single-document summarization tasks. Both approaches are implemented in our general framework driven by sentence compression. Instead of a purely extractive approach, we employ linguistic and statistical methods to generate multiple compressions, and then select from those candidates to produce a final summary. We demonstrate our techniques on the Enron collection—a very challenging corpus because of the highly technical language. Results suggest that CMS represents a better approach and additional findings pave the way for future explorations.

Second Author Affiliation / Address line 1 Affiliation / Address line 2

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Unsupervised Topic Modelling for Multi-Party Spoken Discourse BLIND REVIEW NO First Author NO Affiliation / Address line 1 NO Affiliation / Address line 2

We present a method for unsupervised topic modelling which adapts methods used in document classification (Blei et al., 2003; Griffiths and Steyvers, 2004) to unsegmented multi-party discourse transcripts. We show how Bayesian inference in this generative model can be used to simultaneously address the problems of topic segmentation and topic identification; automatically segmenting multiparty meetings into topically coherent segments with performance which compares well with previous unsupervised segmentation-only methods (Galley et al., 2003); while simultaneously extracting topics which rate highly when assessed for coherence by human judges. We also show that this method appears robust in the face of off-topic dialogue and speech recognition errors.