The Resource Methods for mining and summarizing text conversations, Giuseppe Carenini, Gabriel Murray, and Raymond Ng, (electronic resource)

Methods for mining and summarizing text conversations, Giuseppe Carenini, Gabriel Murray, and Raymond Ng, (electronic resource)

Methods for mining and summarizing text conversations
Methods for mining and summarizing text conversations
Statement of responsibility
Giuseppe Carenini, Gabriel Murray, and Raymond Ng
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
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Additional physical form
Also available in print
Cataloging source
Dewey number
no index present
LC call number
LC item number
.C275 2011
Literary form
non fiction
Nature of contents
  • dictionaries
  • abstracts summaries
  • bibliography
Series statement
Synthesis lectures on data management,
Series volume
Target audience
Methods for mining and summarizing text conversations, Giuseppe Carenini, Gabriel Murray, and Raymond Ng, (electronic resource)
  • Part of: Synthesis digital library of engineering and computer science
  • Series from website
Bibliography note
Includes bibliographical references (p. 107-118)
  • net
  • 1. Introduction -- 1.1 The rise of text conversations -- 1.1.1 The internet revolution -- 1.1.2 The speech technology revolution -- 1.2 Application scenarios -- 1.3 Related topics and background readings -- 1.4 Mining and summarizing text conversations: an overview -- 1.4.1 Mining text conversations -- 1.4.2 Summarizing text conversations -- 1.5 Book preview --
  • 2. Background: corpora and evaluation methods -- 2.1 Corpora and annotations -- 2.1.1 Meeting corpora -- 2.1.2 Email corpora -- 2.1.3 Blog corpora -- 2.2 Evaluation metrics for text mining -- 2.2.1 Precision, recall and f-scores -- 2.2.2 ROC curves -- 2.3 Evaluation metrics for summarization -- 2.3.1 Intrinsic summarization evaluation -- 2.3.2 Extrinsic summarization evaluation -- 2.3.3 Linguistic quality evaluation -- 2.3.4 Evaluation metrics for summarization: a final overview -- 2.4 Conclusion -- 2.5 Important points -- 2.6 Further reading --
  • 3. Mining text conversations -- 3.1 Introduction -- 3.2 Topic modeling: topic segmentation and topic labeling -- 3.2.1 Topic modeling of generic text -- 3.2.2 Topic modeling of conversations -- 3.3 Sentiment and subjectivity detection -- 3.3.1 Sentiment detection background -- 3.3.2 Sentiment detection in conversations -- 3.4 Conversational structure -- 3.4.1 Unique features of human conversations -- 3.4.2 Dialogue act modeling -- 3.4.3 Decision detection -- 3.4.4 Action item detection -- 3.4.5 Extracting the conversational structure -- 3.5 Conclusion -- 3.6 Important points -- 3.7 Further reading --
  • 4. Summarizing text conversations -- 4.1 Introduction -- 4.2 Summarization framework and background -- 4.2.1 Assumptions and inputs -- 4.2.2 Measuring informativeness -- 4.2.3 Outputs and interfaces -- 4.3 Summarizing conversations in one domain -- 4.3.1 Summarizing emails -- 4.3.2 Summarizing meetings -- 4.3.3 Summarizing chats and blogs -- 4.4 Summarizing multi-domain conversations -- 4.4.1 Abstractive conversation summarization: a detailed case study -- 4.5 Conclusion -- 4.6 Important points -- 4.7 Further reading --
  • 5. Conclusions/final thoughts -- Bibliography -- Authors' biographies
Control code
1 online resource (x, 120 p.)
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System requirements: Adobe Acrobat Reader

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