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

本计画提出一个多层次的摘要过程，其特色为：（1）整合以统计与符号技术所衍生的文献知识;（2）使用语言产生法将此资讯重新组合成指示性与资讯性摘要;（3）跨相关文献集的摘要;（4）领域技术的发展 独立 总结。 的 提出 研究包括 两 文献分析与综述 一代 文档分析涉及将文档分割成主题，使用统计技术来识别每个部分中的关键内容，并构建由摘要生成器使用的词汇语义表示。生成侧重于集成 相关 来自几个国家的信息 数据 来源， 关于来自输入文章的对比信息的呈现，以及关于使用规划算子将来自单独表示的信息联合收割机组合成单个简明文本。 该研究包括评估技术， 分析器和生成器的覆盖率和鲁棒性，包括概念和语言知识。 通过与哥伦比亚信息访问研究中心的合作， 拟议 研究 对 动态 产生的， 跨领域的连贯摘要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。

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.