CAREER: Capturing Content and Linguistic Quality in Automatic Extractive and Abstractive Summarization

职业：在自动提取和抽象摘要中捕获内容和语言质量

• 批准号: 0953445
• 负责人: Ani Nenkova
• 金额: $ 54.99万
• 依托单位: University of Pennsylvania
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-02-01 至 2017-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0953445&HistoricalAwards=false
• 关键词: CAREER; Capturing; Content; Linguistic; Quality

This CAREER proposal deals with the development of novel systems for automatic summarization which incorporate both linguistic and content quality considerations in their operation. The main motivation for the work is that even the best current systems do not take the characteristics of the input into account during their operation, they cannot estimate how successful they perform content selection, and completely ignore issues of linguistic quality of the output.Improvement of linguistic quality of summaries requires a combination and relative assessment of a wide range of text quality factors:discourse relations, topic/entity/word coherence, form of referring expressions, vocabulary. Tools for automatic extraction of such models from the input text, including automatic discourse analysis of explicit and implicit discourse relations, are developed as part of the project. The resulting models of linguistic quality will have broader impact on a whole range of text producing applications including questions answering, machine translation, automatic essay grading and computer-assisted writing tutoring.Improvement of content quality requires taking into account characteristics of the input. In particular, we develop measures of input difficulty, which enable systems to automatically predict if they can produce a good quality summary for a given input and permit for change of summarization strategy when necessary. Specialized summarization strategies for input types where current system performance is known to be suboptimal are also elaborated.Text quality and summarization are research topics with cross-disciplinary appeal. The PI will offer project-based courses at the undergraduate and graduate level which have the potential to attract young people to the field of computer science.

这个职业生涯的建议涉及到自动摘要的新系统的开发，其中包括在其操作的语言和内容质量的考虑。 这项工作的主要动机是，即使是目前最好的系统在运行过程中也没有考虑到输入的特征，它们无法估计它们执行内容选择的成功程度，并且完全忽略了输出的语言质量问题。摘要的语言质量的改进需要对广泛的文本质量因素进行组合和相对评估：语篇关系、话题/实体/词汇连贯、指称表达形式、词汇。作为该项目的一部分，开发了从输入文本中自动提取这种模型的工具，包括对显式和隐式话语关系的自动话语分析。由此产生的语言质量模型将对整个文本生成应用程序产生更广泛的影响，包括问答，机器翻译，自动论文评分和计算机辅助写作辅导。内容质量的提高需要考虑输入的特征。特别是，我们开发的措施，输入难度，使系统能够自动预测，如果他们可以产生一个良好的质量摘要为给定的输入，并允许在必要时改变总结策略。专门的摘要输入类型，目前的系统性能是次优的策略也阐述。文本质量和摘要是跨学科的研究课题。PI将在本科和研究生阶段提供基于项目的课程，有可能吸引年轻人进入计算机科学领域。