Methods for Mining and Summarizing Text Conversations

Dr. Giuseppe Carenini is an associate professor in computer science at UBC, with broad interdisciplinary interests. His work on combining natural language processing and information visualization to support decision making has been published in over 70 peer-reviewed papers. Dr. Carenini was the area chair for "Sentiment Analysis, Opinion Mining, and Text Classification" of ACL 2009 and he is currently co-editing an ACM TIST Special Issue on Intelligent Visual Interfaces for Text Analysis. In his work, Dr. Carenini has also extensively collaborated with industrial part[1]ners, including Microsoft and IBM. Dr. Gabriel Murray is a researcher in computer science at UBC, in the Laboratory for Computational Intelligence. His back[1]ground is in natural language processing as well as theoretical linguistics. He has an established research record in the area of automatic summarization, with particular attention to summarization of noisy genres such as speech and web data, and comparison of abstractive and extractive techniques. He did his graduate studies at the University of Edinburgh under Dr. Steve Renals, and was a member of the EU-funded AMI project on studying multi[1]modal interaction. He is currently a researcher with the NSERC Business Intelligence Network on intelligent data management and decision making. Dr. Raymond Ng is a professor in computer science at UBC. He is internationally renowned for his data mining studies. He has published over 100 journal and conference papers covering a broad range of topics in informatics, data mining and databases. He has won Best Paper awards from the ACM SIGKDD conference on data mining and the ACM SIGMOD conference on database management. For the past few years, Dr. Ng has been one of the editors of two top database journals worldwide-the VLDB Journal and the IEEE Transactions on Knowledge and Data Engineering. He was the general chair of ACM SIGMOD 2008 and the program chair of IEEE ICDE 2009.

Due to the Internet Revolution, human conversational data -- in written forms -- are accumulating at a phenomenal rate. At the same time, improvements in speech technology enable many spoken conversations to be transcribed. Individuals and organizations engage in email exchanges, face-to-face meetings, blogging, texting and other social media activities. The advances in natural language processing provide ample opportunities for these "informal documents" to be analyzed and mined, thus creating numerous new and valuable applications. This book presents a set of computational methods to extract information from conversational data, and to provide natural language summaries of the data. The book begins with an overview of basic concepts, such as the differences between extractive and abstractive summaries, and metrics for evaluating the effectiveness of summarization and various extraction tasks. It also describes some of the benchmark corpora used in the literature. The book introducesextraction and mining methods for performing subjectivity and sentiment detection, topic segmentation and modeling, and the extraction of conversational structure. It also describes frameworks for conducting dialogue act recognition, decision and action item detection, and extraction of thread structure. There is a specific focus on performing all these tasks on conversational data, such as meeting transcripts (which exemplify synchronous conversations) and emails (which exemplify asynchronous conversations). Very recent approaches to deal with blogs, discussion forums and microblogs (e.g., Twitter) are also discussed. The second half of this book focuses on natural language summarization of conversational data. It gives an overview of several extractive and abstractive summarizers developed for emails, meetings, blogs and forums. It also describes attempts for building multi-modal summarizers. Last but not least, the book concludes with thoughts on topics for further development. Table of Contents: Introduction / Background: Corpora and Evaluation Methods / Mining Text Conversations / Summarizing Text Conversations / Conclusions / Final Thoughts

Introduction.- Background: Corpora and Evaluation Methods.- Mining Text Conversations.- Summarizing Text Conversations.- Conclusions.- Final Thoughts.