Mode of access: World Wide Web.

System requirements: Adobe Acrobat Reader.

Title from web page (viewed February 28, 2019).

Includes bibliographical references and index.

Multilingual text analysis : history, tasks and challenges -- Using a polytope model for unsupervised document summarization -- Approach for unsupervised multilingual document summarization -- Rich feature spaces and regression models in single-document extractive summarization -- Hierarchical topic model and summarization -- A survey of neural models for abstractive summarization -- Heads : headline generation as a sequence prediction using an abstract feature-rich space -- Crowdsourcing in single-document summary -- Multilingual summarization and evaluation using Wikipedia featured articles -- Are better summaries also easier to understand? : analyzing text complexity -- In automatic summarization -- Twitter event detection, analysis, and summarization -- Linguistic bias in crowdsourced biographies : a cross-lingual examination -- Multilingual financial narrative processing : analysis annual reports in English, Spanish, and Portugese.

"Text analytics (TA) covers a very wide research area. Its overarching goal is to discover and present knowledge - facts, rules, and relationships - that is otherwise hidden in the textual content. The authors of this book guide us in a quest to attain this knowledge automatically, by applying various machine learning techniques. This book describes recent development in multilingual text analysis. It covers several specific examples of practical TA applications, including their problem statements, theoretical background, and implementation of the proposed solution. The reader can see which preprocessing techniques and text representation models were used, how the evaluation process was designed and implemented, and how these approaches can be adapted to multilingual domains."-- Publisher's website.