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

本项目提出了一个多层次的总结过程，其特点是：(1)整合使用统计和符号技术派生的关于文档的知识；(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)相关文章集间的汇总；(4)领域无关摘要技术的发展。本研究包括文献分析和摘要生成两部分。文档分析包括将文档分割为多个主题，使用统计技术识别每个部分中的关键内容，并构建供摘要生成器使用的词法语义表示。生成侧重于整合来自多个数据源的相关信息，展示来自输入条目的对比信息，以及使用规划操作符将来自单独表示的信息组合成一个简洁的文本。该研究包括测量分析器和生成器的覆盖率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获取研究中心的合作，对跨领域动态生成的连贯摘要的拟议研究将最终成为数字图书馆界面的一部分，该界面可以根据用户的兴趣和需求过滤文档。

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.