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.

这个I-Corps项目的更广泛的影响/商业潜力集中在改善人类的处理和理解长期复杂文档中包含的信息。利用NSF资助的数字图书馆中的研究，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），该项目将评估此类技术（特别关注摘要）是否会导致保险和法律行业内的新商机。这些行业之间的一种联系是共享长期，通常复杂的文件。将摘要方法与信息检索相结合，并进一步利用文本分析和采矿技术，可以更快，更有效地管理这些文档。从更广泛的角度来看，为长期文档创建有用的摘要的问题适用于许多企业，包括出版行业，在该行业中，所提出的技术可以帮助作者和读者，以及参与发现，搜索和管理已发表的文档收集的涉及的技术。该I-Corps项目将如何在商业设置中改进和应用高级数字图书馆。援助管理和理解信息的重要服务是总结。尽管最近的提取性自动化（例如，从文档中选择一组最重要的句子）和抽象的（例如，像人类的抽象者一样自动准备摘要）摘要方法，这些方法应用于新闻文章或其他简短但流行的作品，但仅在应用于更长的文档时就可以产生很多实际的成功。设计和验证用于为长文档构建抽象性摘要的有效技术是一个非常困难的问题，这是该项目努力的重点。此外，使这些技术与各种域中的复杂文档一起使用是一个特殊的挑战。将此类工作与大数据收集，在线云系统，可解释的人工智能方法和功能性用户界面联系起来，是在具有商业利益的新环境中必须解决的其他挑战。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的智力和更广泛影响的评估来通过评估来支持的，这是值得的。