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IEEE Computer Society Bangalore Chapter - BITS APPCAIR - Joint Webinar Series 2020 | Session 01 Advanced AI/ML: Unsupervised Techniques for Search Result Clustering and Summarization About the topic: This talk will cover different multi-objective based clustering techniques and their applications in search result clustering and summarization. Multiobjective optimization aims to optimize multiple objective functions simultaneously. Unsupervised classification problem can also be modeled as a multiobjective optimization problem where the objective is to optimize multiple cluster quality measures simultaneously. In recent years several such multiobjective clustering approaches have been developed and their effectiveness has been shown for solving problems of search result clustering, word sense disambiguation etc. Apart from the textual view cued by both the semantic and syntactic information, a complementary view extracted from images contained in the web snippet can also be utilized for grouping the web-snippets for search result clustering. Similarly, the extractive summarization problem can also be formulated as a multiobjective optimization problem where the task is to automatically select a suitable set of sentences covering the entire theme of the document, maximizing relevancy and minimizing the redundancy. About the speaker: Dr. Sriparna Saha received M.Tech and Ph.D. degrees in computer science from Indian Statistical Institute Kolkata, in 2005 and 2011, respectively. She is currently a Faculty Member in the Department of Computer Science and Engineering, Indian Institute of Technology Patna. She has authored or coauthored more than 200 papers in reputed international conferences and journals. Her current research interests include text mining, machine learning, multiobjective optimization, and biomedical information extraction. She is a senior member of IEEE and Associate Editor of ACM Transactions on Asian and Low Resource Language Information Processing. Further info: www.iitp.ac.in/~sriparna