CAREER: Capturing Content and Linguistic Quality in Automatic Extractive and Abstractive Summarization
CAREER: Capturing Content and Linguistic Quality in Automatic Extractive and Abstractive Summarization
批准号:
0953445
负责人:
Ani Nenkova
金额:
$54.99万
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
项目状态:
已结题
起止时间:
2010-02-01 至 2017-01-31
中文摘要
这份职业提案涉及开发用于自动摘要的新系统,该系统在操作中纳入了语言和内容质量两方面的考虑。这项工作的主要动机是,即使是最好的系统,在其操作过程中也没有考虑到输入的特点,它们无法估计它们进行内容选择的成功程度,并且完全忽视了输出的语言质量问题。提高摘要的语言质量需要结合和相对评估广泛的文本质量因素:语篇关系、主题/实体/词的连贯、指称表达的形式、词汇。作为该项目的一部分,开发了从输入文本中自动提取此类模型的工具,包括对显性和隐性语篇关系的自动语篇分析。由此产生的语言质量模型将对包括问题回答、机器翻译、自动作文评分和计算机辅助写作辅导在内的整个文本生成应用程序产生更广泛的影响。提高内容质量需要考虑输入的特点。特别是,我们开发了输入难度的测量方法,使系统能够自动预测它们是否能够为给定的输入生成高质量的摘要,并允许在必要时更改摘要策略。文中还详细阐述了针对当前系统性能不佳的输入类型的专门摘要策略。文本质量和摘要是具有跨学科吸引力的研究课题。国际计算机协会将在本科生和研究生阶段提供以项目为基础的课程,这些课程有可能吸引年轻人进入计算机科学领域。
英文摘要
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.
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会议论文
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