I-Corps: Automated Summarization Technology

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The broader impact/commercial potential of this I-Corps project is focused on improving human handling and understanding of information contained in long and complex documents. Drawing upon NSF-funded research in digital libraries which apply information retrieval and artificial intelligence techniques (such as natural language processing, machine learning, and deep learning), this project will evaluate whether such technologies (with a particular focus on summarization) can lead to new business opportunities within both the insurance and legal industry. One connection between these industries is the sharing of long, often complex, documents. Integrating summarization methods with information retrieval, and further leveraging text analysis and mining technologies, could aid these businesses in more quickly and efficiently managing such documents. More broadly, the problem of creating a useful summary for a long document is applicable to many businesses, including the publishing industry, where the proposed technology could assist both authors and readers, as well as those involved in discovering, searching, and managing published document collections.This I-Corps project will address how advanced digital library methods can be improved and applied in commercial settings. An important service to aid with managing and understanding information is summarization. While there has been recent automation of extractive (e.g., selecting a set of the most important sentences from a document) and abstractive (e.g., automatically preparing an abstract as would be done by a human abstracter) summarization methods, as applied to news articles or other short but popular works, only extractive methods have yielded much real success when applied to longer documents. Devising and validating effective techniques for constructing abstractive summaries for long documents is a very difficult problem, which is a key focus of this project's efforts. Further, making those techniques work with complex documents in a variety of domains is a special challenge. Connecting such efforts with big data collections, online cloud systems, explainable artificial intelligence methods, and functional user interfaces, are additional challenges that must be addressed in new settings that are of commercial interest.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.