The abundance of unstructured information raises the need for automatic systems that can “condense” information from various documents into a shorter length, readable summary. Such summaries may further be required to cover a specific information need (e.g., summarizing web search results, question answering).
Various methods have been proposed for the query-focused summarization task. These methods can be categorized based on two main dimensions namely: extractive vs. abstractive and supervised vs. unsupervised.
In this talk we will focus on extractive, query focused, unsupervised mutli-document summarization techniques. We will survey some of the recent works and their applications in the industry in general and in IBM in particular. I will then describe a state of the art summarization approach that is being developed in our lab.
This talk is based on a work published in SIGIR 2017.
Dr. Guy Feigenblat is a team leader at the Language and Retrieval group in IBM Research AI.
In his current position Guy mainly focuses on the development of automatic document summarization algorithms (query-based, extractive, abstractive) for various domains and use cases. Prior to that he was involved in developing cognitive bots that can express and predict human emotions. Guy was also leading a project that focused on indoor location analyses and how to predict customer’s behavior in virtual and physical venues.
Guy holds a Ph.D. in computer science from Bar-Ilan University, under the supervision of Prof. Ely Porat. In the academia his research focused on data structures, hash-functions, pattern matching and data mining.