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

在当今的信息社会中，我们可以接触到大量的信息。如果没有适当的理论、技术和工具以清晰和有用的方式获取我们需要的信息，信息过载或信息疲劳的风险就会不断增加，从而导致信息使用不良和决策效率低下。***目前在自然语言处理和人类语言技术领域有几个我感兴趣的研究问题。我打算继续关注我以前研究过的领域中的问题，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，以提供适当的和与用户相关的信息，避免信息过载，并协助知情决策。这将导致建立捕捉用户需求和偏好的系统，并生成定制的、个性化的文档。在这个提案中，我解决了这些新的需求和要求，我计划从与文本摘要、问题回答和问题生成相关的角度来研究这些目标。我认为二者具有很强的互补性，需要从以下几个目标和方面进行研究和探索。在短期内，开发能够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，从抽取式摘要向抽象式摘要转变，研究摘要技术对大数据的可扩展性。长期捕获用户需求并将摘要裁剪为用户需求和意图的答案，建立从主题生成和定制问题的机制，设计处理自然语言的智能机制，即推理、综合、要点、浓缩和总结信息，以及连贯和定制的信息传递机制。能使我们实现这些目标的科学方法有很多种。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数据的表示，这些表示可以对文本数据进行高级抽象建模，并且这些表示可以捕获意义。另一方面，我们打算探索基于词汇、句法和语义知识的句子结构，目的是压缩、约简、列出和抽象所选择的内容，用于摘要和答案。此外，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。* * * * *

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.*** **