Inferencing and synthesizing information from multiple documents using text summarization and question answering models

使用文本摘要和问答模型从多个文档中推断和合成信息

• 批准号: 228139-2011
• 负责人: Chali, Yllias
• 金额: $ 1.46万
• 依托单位: University of Lethbridge
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2014
• 资助国家: 加拿大
• 起止时间: 2014-01-01 至 2015-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=546891
• 关键词: Inferencing; synthesizing; information; multiple; documents

The term "Google" has become a verb for most of us. Search engines, however, have certain limitations. For example ask it for the impact of the current global financial crisis in different parts of the world, and you can expect to sift through thousands of results for the answer. This motivates the research in complex question answering where the purpose is to create summaries of large volumes of information as answers to complex questions, rather than simply offering a listing of sources. Unlike simple questions, complex questions cannot be answered easily as they often require inferencing and synthesizing information from multiple documents. Inferencing and synthesizing information from multiple documents require extracting, organizing, and inter-relating the pieces of information contained in a set of relevant documents, in order to obtain a comprehensive, non redundant report that satisfies the information need. Answering complex questions can be seen as a topic-oriented, informative multi-document summarization where the goal is to produce a single text as a compressed version of a set of documents with a minimum loss of relevant information. This research proposal deals with ways in which text summarization and question answering technologies can be combined to form truly useful information delivery tools. More precisely, the problems that we are planning to investigate are: developing text summarization and question answering models that answer complex questions, building decomposition models that decompose complex questions into simple questions easy to answer, constructing models that generate questions automatically, capturing users' needs and tailoring the answers as summaries to user needs, and designing intelligent mechanisms to process natural language (i.e., inference, synthesize and summarize information) as well as mechanisms for coherent and tailored information delivery. I am planning to develop models, technologies and tools to deliver to users information that is relevant and appropriate for the users receiving it, information that they can easily assimilate to enable them to perform their tasks, usually enabling better and more efficient decision-making.

对我们大多数人来说，“谷歌”已经成为一个动词。然而，搜索引擎有一定的局限性。例如，问它当前全球金融危机对世界不同地区的影响，你可以期望从成千上万的结果中筛选答案。这激发了对复杂问题回答的研究，其目的是创建大量信息的摘要，作为复杂问题的答案，而不是简单地提供来源列表。与简单问题不同，复杂问题不容易回答，因为它们通常需要从多个文档中推断和综合信息。从多个文档中推断和综合信息，需要对一组相关文档中包含的信息片段进行提取、组织和相互关联，以获得满足信息需求的全面、无冗余的报告。回答复杂的问题可以看作是一个面向主题的、信息丰富的多文档摘要，其目标是产生一个单一的文本，作为一组文档的压缩版本，将相关信息的损失降到最低。本研究计划探讨如何将文本摘要和问答技术结合起来，形成真正有用的信息传递工具。更准确地说，我们计划调查的问题是：开发能够回答复杂问题的文本摘要和问答模型，构建能够将复杂问题分解为易于回答的简单问题的分解模型，构建自动生成问题的模型，捕捉用户需求并将答案裁剪为符合用户需求的摘要，设计处理自然语言（即推理）的智能机制；综合和总结信息)以及连贯和量身定制的信息传递机制。我计划开发模型、技术和工具，向用户提供与接收信息的用户相关且合适的信息，这些信息可以很容易地吸收，使他们能够执行任务，通常可以实现更好、更有效的决策。