STIMULATE: Generating Coherent Summaries of On-Line Documents: Combining Statistical and Symbolic Techniques

刺激：生成在线文档的连贯摘要：结合统计和符号技术

• 批准号: 9618797
• 负责人: Kathleen McKeown
• 金额: $ 43.89万
• 依托单位: Columbia University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 1997
• 资助国家: 美国
• 起止时间: 1997-03-01 至 2001-02-28
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9618797&HistoricalAwards=false
• 关键词: STIMULATE; Generating; Coherent; Summaries; Line

This project proposes a multi-level process of summarization featuring: (1) integration of knowledge about a document derived with both statistical and symbolic techniques; (2) the use of language generation for reformulating this information into both indicative and informative summaries; (3) summarization across sets of related articles; and (4) development of techniques for domain independent summarization. The proposed research comprises both document analysis and summary generation. Document analysis involves segmenting a document into topics, using statistical techniques to identify key content within each section, and constructing a lexical semantic representation for use by the summary generator. Generation focuses on integration of relevant information from several data sources, on presentation of contrasting information from the input articles, and on the use of planning operators to combine information from the separate representations into a single concise text. The research includes evaluation techniques for measuring both coverage and robustness of the analyzer and generator, including conceptual and linguistic knowledge. Through collaboration with Columbia's Center for Research on Information Access, the proposed research on dynamically generated, coherent summarization across domains will ultimately be part of a digital library interface that filters documents according to a user's interests and needs.

该项目提出了一个多层次的总结过程，其特点是：（1）整合统计和符号技术导出的文档知识； (2) 使用语言生成将这些信息重新表述为指示性和信息性摘要； (3) 跨组相关文章的总结； (4) 领域独立摘要技术的开发。 拟议的研究包括文档分析和摘要生成。 文档分析涉及将文档分段为主题，使用统计技术来识别每个部分中的关键内容，以及构建供摘要生成器使用的词汇语义表示。生成侧重于来自多个数据源的相关信息的集成，来自输入文章的对比信息的呈现，以及使用规划运算符将来自单独表示的信息组合成单个简洁的文本。 该研究包括用于测量分析器和生成器的覆盖范围和鲁棒性的评估技术，包括概念和语言知识。 通过与哥伦比亚大学信息访问研究中心的合作，拟议的跨领域动态生成、连贯摘要的研究最终将成为数字图书馆界面的一部分，该界面根据用户的兴趣和需求过滤文档。