Grant: Discovery Grants Program - Individual

In today's information society, we have access to an enormous amount of information. Without appropriate theories, technologies, and tools to obtain the information we need in a clear and useful way, there is an ever increasing risk of information overload or information fatigue, leading to poor information use and inefficiencies in decision-making.*There are several research problems that are of current interest to me in the fields of natural language processing and human language technologies. I intend to continue to focus on problems in the areas that I have previously investigated, namely text summarization, complex question answering, and question generation. I am interested in developing techniques for delivering information that is appropriate and relevant to the user, avoiding information overload and assisting informed decision making. This will lead to build up systems that capture user needs and preferences and generate customized, personalized documents. *In this proposal, I address these new needs and demands and I plan to study these objectives from the points of views that relate text summarization, question answering, and question generation. I contend that they hold a strong complementarity which has to be studied and explored according to the following goals and aspects. In a short-term, developing text summarization and question answering models that answer complex questions, building decomposition models that break down complex questions into simpler questions that are easy to answer, constructing models that generate automatically questions, shifting from extractive summarization to abstractive summarization, studying the scalability of the summarization techniques to large amount of data. In a long-term capturing users' needs and tailoring the summaries as answers to user needs and intents, having mechanisms that generate and tailor questions from topics, and designing intelligent mechanisms to process natural language, i.e. inference, synthesize, gist, condense and summarize information as well as mechanisms for coherent and tailored information delivery.*The scientific approaches that will allow us to achieve these goals are of different kinds. On the one hand, we intend to explore methods based on machine learning techniques, especially methods that involve the deep learning techniques since these methods showed that they are able to acquire representations of data that model high-level abstractions of textual data and those representations capture meaning. On the other hand, we intend to explore the structures of sentences based on lexical, syntactic and semantic knowledge for the purpose of compressing, reducing, gisting, and abstracting the selected content for the summaries and the answers. Also we intend to explore frameworks that allow us to process large amounts of textual data for the purpose of the scalability of the summarization techniques.*