I-Corps: Automated Summarization Technology

I-Corps：自动摘要技术

• 批准号: 1924726
• 负责人: Edward Fox
• 金额: $ 5万
• 依托单位: Virginia Polytechnic Institute and State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-04-01 至 2021-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1924726&HistoricalAwards=false
• 关键词: Corps; Automated; Summarization; Technology

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.

该 I-Corps 项目更广泛的影响/商业潜力侧重于改善人类对长而复杂的文档中包含的信息的处理和理解。该项目利用 NSF 资助的数字图书馆研究，这些研究应用信息检索和人工智能技术（例如自然语言处理、机器学习和深度学习），将评估这些技术（特别关注摘要）是否可以带来保险和法律行业的新商机。这些行业之间的联系之一是共享冗长且通常很复杂的文档。将摘要方法与信息检索相结合，并进一步利用文本分析和挖掘技术，可以帮助这些企业更快、更有效地管理此类文档。更广泛地说，为长文档创建有用摘要的问题适用于许多企业，包括出版业，其中所提出的技术可以帮助作者和读者，以及涉及发现、搜索和管理已发布文档的人员该 I-Corps 项目将解决如何改进先进的数字图书馆方法并将其应用于商业环境。帮助管理和理解信息的一项重要服务是摘要。虽然最近出现了提取（例如，从文档中选择一组最重要的句子）和抽象（例如，像人类摘要者一样自动准备摘要）摘要方法的自动化，如应用于新闻文章或在其他简短但流行的作品中，只有提取方法在应用于较长的文档时才取得了真正的成功。设计和验证为长文档构建抽象摘要的有效技术是一个非常困难的问题，也是该项目工作的重点。此外，使这些技术适用于各个领域的复杂文档是一个特殊的挑战。将这些努力与大数据收集、在线云系统、可解释的人工智能方法和功能用户界面联系起来，是在具有商业利益的新环境中必须解决的额外挑战。该奖项反映了 NSF 的法定使命，并被认为是值得的通过使用基金会的智力优势和更广泛的影响审查标准进行评估来提供支持。