This work concerns automatic topic segmentation of email conversations . We present a corpus of email threads manually annotated with topics , and evaluate annotator reliability . To our knowledge , this is the first such email corpus . We show how the existing topic segmentation models ( i.e. , Lexical Chain Segmenter ( LCSeg ) and Latent Dirichlet Allocation ( LDA ) ) which are solely based on lexical information , can be applied to emails . By pointing out where these methods fail and what any desired model should consider , we propose two novel extensions of the models that not only use lexical information but also exploit finer level conversation structure in a principled way . Empirical evaluation shows that LCSeg is a better model than LDA for segmenting an email thread into topical clusters and incorporating conversation structure into these models improves the performance significantly . With the ever increasing popularity of emails and web technologies , it is very common for people to discuss issues , events , agendas or tasks by email . Effective processing of the email contents can be of great strategic value . In this paper , we study the problem of topic segmentation for emails , i.e. , grouping the sentences of an email thread into a set of coherent topical clusters . Adapting the standard definition of topic ( Galley et al . , 2003 ) to conversations / emails , we consider a topic is something about which the participant(s ) discuss or argue or express their opinions . For example , in the email thread shown in Figure 1 , according to the majority of our annotators , participants discuss three topics ( e.g. , ' telecon cancellation ' , ' TAG document ' , and ' responding to I18N ' ) . Multiple topics seem to occur naturally in social interactions , whether synchronous ( e.g. , chats , meetings ) or asynchronous ( e.g. , emails , blogs ) conversations . In multi-party chat ( Elsner and Charniak , 2008 ) report an average of 2.75 discussions active at a time . In our email corpus , we found an average of 2.5 topics per thread . Topic segmentation is often considered a prerequisite for other higher-level conversation analysis and applications of the extracted structure are broad , encompassing : summarization ( Harabagiu and Lacatusu , 2005 ) , information extraction and ordering ( Allan , 2002 ) , information retrieval ( Dias et al . , 2007 ) , and intelligent user interfaces ( Dredze et al . , 2008 ) . While extensive research has been conducted in topic segmentation for monologues ( e.g. , ( Malioutov and Barzilay , 2006 ) , ( Choi et al . , 2001 ) ) and synchronous dialogs ( e.g. , ( Galley et al . , 2003 ) , ( Hsueh et al . , 2006 ) ) , none has studied the problem of segmenting asynchronous multi-party conversations ( e.g. , email ) . Therefore , there is no reliable annotation scheme , no standard corpus , and no agreedupon metrics available . Also , it is our key hypothesis that , because of its asynchronous nature , and the use of quotation ( Crystal , 2001 ) , topics in an email thread often do not change in a sequential way . As a result , we do not expect models which have proved successful in monologue or dialog to be as effective when they are applied to email conversations . Our contributions in this paper aim to remedy these problems . First , we present an email corpus annotated with topics and evaluate annotator agreement . Second , we adopt a set of metrics to measure the local and global structural similarity between two annotations from the work on multi-party chat disentanglement ( Elsner and Charniak , 2008 ) . Third , we show how the two state-of-the-art topic segmentation methods ( i.e. , LCSeg and LDA ) which are solely based on lexical information and make strong assumptions on the resulting topic models , can be effectively applied to emails , by having them to consider , in a principled way , a finer level structure of the underlying conversations . Experimental results show that both LCSeg and LDA benefit when they are extended to consider the conversational structure . When comparing the two methods , we found that LCSeg is better than LDA and this advantage is preserved when they are extended to incorporate conversational structure . In this paper we presented an email corpus annotated for topic segmentation . We extended LDA and LC-Seg models by incorporating the fragment quotation graph , a fine-grain model of the conversation , which is based on the analysis of quotations . Empirical evaluation shows that the fragment quotation graph helps both these models to perform significantly better than their basic versions , with LCSeg+FQG being the best performer .

Challenge: Because of the absence of a corpus, scheme or metrics, there is no work on topic segmentation for asynchronous multi-party conversations.
Approach: They present the first email corpus with topic annotations and evaluation metrics, then evaluate existing methods and their extensions that utilize conversation structure features.
Outcome: The existing LDA-based methods with conversation structure features perform well on the new email topic segmentation corpus.