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 introduces extraction
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
