Abstracting, Gisting, Tailoring, and Delivering Information Contents to User Needs and Intents

根据用户需求和意图抽象、要点、定制和交付信息内容

• 批准号: RGPIN-2016-06434
• 负责人: Chali, Yllias
• 金额: $ 2.26万
• 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: 2019
• 资助国家: 加拿大
• 起止时间: 2019-01-01 至 2020-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=683738
• 关键词: Abstracting; Gisting; Tailoring; Delivering; Information

In today's information society, we have access to an enormous amount of information. Without appropriate theories, technologies, and tools to obtain the information we need in a clear and useful way, there is an ever increasing risk of information overload or information fatigue, leading to poor information use and inefficiencies in decision-making.***There are several research problems that are of current interest to me in the fields of natural language processing and human language technologies. I intend to continue to focus on problems in the areas that I have previously investigated, namely text summarization, complex question answering, and question generation. I am interested in developing techniques for delivering information that is appropriate and relevant to the user, avoiding information overload and assisting informed decision making. This will lead to build up systems that capture user needs and preferences and generate customized, personalized documents. ***In this proposal, I address these new needs and demands and I plan to study these objectives from the points of views that relate text summarization, question answering, and question generation. I contend that they hold a strong complementarity which has to be studied and explored according to the following goals and aspects. In a short-term, developing text summarization and question answering models that answer complex questions, building decomposition models that break down complex questions into simpler questions that are easy to answer, constructing models that generate automatically questions, shifting from extractive summarization to abstractive summarization, studying the scalability of the summarization techniques to large amount of data. In a long-term capturing users' needs and tailoring the summaries as answers to user needs and intents, having mechanisms that generate and tailor questions from topics, and designing intelligent mechanisms to process natural language, i.e. inference, synthesize, gist, condense and summarize information as well as mechanisms for coherent and tailored information delivery.***The scientific approaches that will allow us to achieve these goals are of different kinds. On the one hand, we intend to explore methods based on machine learning techniques, especially methods that involve the deep learning techniques since these methods showed that they are able to acquire representations of data that model high-level abstractions of textual data and those representations capture meaning. On the other hand, we intend to explore the structures of sentences based on lexical, syntactic and semantic knowledge for the purpose of compressing, reducing, gisting, and abstracting the selected content for the summaries and the answers. Also we intend to explore frameworks that allow us to process large amounts of textual data for the purpose of the scalability of the summarization techniques.*** **

在当今的信息社会中，我们可以获得大量的信息。如果没有适当的理论、技术和工具来以清晰、有用的方式获取我们所需的信息，信息过载或信息疲劳的风险就会不断增加，从而导致信息利用不良和决策效率低下。***我目前在自然语言处理和人类语言技术领域感兴趣的几个研究问题。我打算继续关注我之前研究过的领域的问题，即文本摘要、复杂问题回答和问题生成。我感兴趣的是开发技术来提供适合用户且与用户相关的信息，避免信息过载并协助做出明智的决策。这将导致建立能够捕获用户需求和偏好并生成定制的个性化文档的系统。 ***在本提案中，我解决了这些新的需求和要求，并计划从与文本摘要、问题回答和问题生成相关的角度研究这些目标。笔者认为，它们具有很强的互补性，需要从以下几个目标和方面进行研究和探索。短期内，开发回答复杂问题的文本摘要和问答模型，构建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，从抽取式摘要转向抽象式摘要，研究摘要技术对大量数据的可扩展性。长期捕获用户的需求并定制摘要作为用户需求和意图的答案，拥有从主题中生成和定制问题的机制，并设计处理自然语言的智能机制，即推理、综合、要点、压缩和总结信息，以及连贯和定制的信息传递机制。***使我们能够实现这些目标的科学方法有多种。一方面，我们打算探索基于机器学习技术的方法，特别是涉及深度学习技术的方法，因为这些方法表明它们能够获取对文本数据的高级抽象进行建模的数据表示，并且这些表示捕获含义。另一方面，我们打算基于词汇、句法和语义知识来探索句子结构，以压缩、减少、概括和抽象摘要和答案所选择的内容。我们还打算探索允许我们处理大量文本数据的框架，以实现摘要技术的可扩展性。*** **