Integrating Methods for Mining and Summarizing Text Conversations

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Due to the Internet Revolution, large numbers of individuals and organizations constantly engage in email exchanges, blogging, texting and other social media activities. At the same time, improvements in speech technology enable many spoken conversations (e.g., face -to-face meetings) to be automatically transcribed. The net result of these trends is that human conversational data-in written forms-are accumulating at a phenomenal rate. Although several computational methods have been proposed to analyze, mine and summarize these informal, conversational documents, they have been limited in four key ways. First, the majority of research on summarizing conversations has been focusing on extractive systems, where a summary is simply a subset of the utterances in the input conversation. Second, most of the proposed methods have been mainly developed for meetings and emails, with little work on other conversational modalities. Third, most of these methods are supervised ones, which need to be trained on annotated corpora. Finally, current conversation mining techniques are very task-specific; for instance methods for topic modeling, for dialog act modeling and for extracting the conversational structure have been developed largely independently. In this proposal, we plan to address all these limitations by developing: - Summarization systems that will be increasingly abstractive, reflecting more closely what people naturally produce and expect. - Methods to transfer data and insights from one conversational modality to another; for instance, from meetings and emails to blogs and micro-blogs. -Semi-supervised and unsupervised machine learning techniques that can be easily applied to blogs and tweets by leveraging large amounts of readily available unlabeled conversational data. -A framework for integrating the different text mining tasks. In particular, we will study how dialog act modeling, topic modeling and sentiment analysis can be performed simultaneously and interdependently.