Text-to-Text Generation for Summarizing Informal Genres

用于总结非正式流派的文本到文本生成

• 批准号: 0534871
• 负责人: Kathleen McKeown
• 金额: --
• 依托单位: Columbia University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-01-01 至 2010-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0534871&HistoricalAwards=false
• 关键词: Text; Generation; Summarizing; Informal; Genres

This project aims at the generation of coherent and on-target summaries and answers through the use of text-to-text generation, an approach which generates new sentences from the input text, fusing relevant phrases and discarding irrelevant ones. A syntactic,statistical framework for text-to-text generation is being developed that can be applied to informal genres, such as transcribed speech and email, where sentences are not guaranteed to be either complete or grammatical. It is exactly these genres that stand to benefit the most from this approach; for them, summarization using sentence extractionalone is not an option.The aim is a fully developed, syntactic statistical framework for text-to-text generation which features the use of a full syntactic grammar within a statistical framework for compression and combination, a model for incorporating constraints from pragmatics andsemantics into the generation system, the ability to produce fluent, grammatical sentences from fragmentary and ungrammatical input, and the ability to generate sentences that make high level abstractions from input document sentences.The project features the integration of compression and language models into a lexicalized head-driven framework, enabling the generator to keep the sentence grammatical and avoid wording changes that dramatically alter meaning. Its framework can incorporate an arbitrary number of features beyond syntax that are important forsummarization. A new dynamic programming technique allows the automatic extraction of large amounts of training data from a summary/document corpus. Information about who speaks to whom and paraphrasing rules will increase the range of revisions that can be addressed.

该项目旨在通过使用文本到文本生成来生成连贯和目标摘要和答案，这种方法从输入文本生成新句子，融合相关短语并丢弃不相关短语。 一个用于文本到文本生成的句法统计框架正在开发中，该框架可以应用于非正式的体裁，例如转录的语音和电子邮件，其中句子不 保证是完整的或合乎语法的。正是这些流派从这种方法中受益最多;对他们来说，仅仅使用句子提取的摘要不是一种选择。其目标是一个完全开发的，用于文本到文本生成的句法统计框架，其特征是在用于压缩和组合的统计框架内使用完整的句法语法，一个将语用和语义的约束纳入生成系统的模型，能够从零碎和不合语法的输入中生成流畅、合乎语法的句子，并能够从输入的文档句子中生成高级抽象的句子。该项目的特点是将压缩和语言模型集成到词汇化的头部驱动框架中，使生成器能够保持句子的语法性，并避免显著改变含义的措辞变化。它的框架可以包含任意数量的语法之外的功能，这些功能对摘要很重要。一种新的动态编程技术允许从摘要/文档语料库中自动提取大量的训练数据。 关于谁对谁说话的信息和解释规则将增加可以解决的修订范围。