Text Summarization and Question Generation Models

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Natural language processing (NLP) is one of the most important technologies of the information age. Understanding complex language utterances is also a crucial part of artificial intelligence. Applications of NLP are everywhere because people communicate mostly everything in language: web searches, news, advertisements, emails, customer services, language translations, radiology reports, social media, virtual assistants, conversational systems (chatbots), blogs, product comments, etc. I intend to continue to focus on problems in the areas that I have previously investigated, namely in the areas of text summarization, and question generation. I am interested in building up systems that on the one hand gist, synthesize, and abstract information content from documents, and on the other hand capture user needs, intents, and preferences and generate customized, personalized documents. Text summarization and question generation are research directions for solving the information overload and the information fatigue problems. This will result in theories, technologies, and tools leading to rich information use and efficient decision-making.  In this proposal, I plan to study the following objectives: in a short-term (1) developing robust representation learning with structure and reasoning, (2) building robust abstractive text summarization models, (3) devising comprehensive question generation models, and constructing models that generate automatically answer-agnostic questions, inquisitive questions, and multiHop questions, (4) studying the complementarity between text summarization and question generation, and in a long-term (1) capturing users' needs and tailoring the summaries as answers to user needs and intents, (2) shifting from the informative summarization to the inferential summarization, and (3) 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. This research proposal deals with ways of combining text summarization and question generation technologies to form truly useful information delivery tools. I am planning to develop models, technologies and tools to deliver to users information that is relevant and appropriate for the users receiving it, information that they can easily assimilate to enable them to perform their tasks, usually enabling better and more efficient decision-making. These tools can be used to monitor, filter, extract, abstract, and detect specific/relevant information and evidence in large amount of textual data, hence, saving time to the end-user. The work requires the involvement of a number of interested undergraduate and graduate students at different stages, and will have several research contributions. The results will improve the state-of-the-art text summarization and question generation models and will be disseminated in peer-reviewed conferences and journals.