RI: Multi-Faceted Comparative Text Summarization

文本摘要生成简明的文档摘要，帮助用户快速消化信息，随着信息的爆炸性增长，文本摘要变得越来越重要。大多数现有的摘要方法只能生成具有简单句子列表的非结构化摘要。然而，在许多应用程序中，我们希望生成(更具结构化的)多方面比较摘要，其中句子被分组到多个方面，并在不同的视图中进行比较。例如，关于笔记本电脑的意见摘要可能会将句子分成几个方面，如“电池寿命”和“内存”，并在每个方面单独使用正面和负面意见。本项目旨在系统地研究这一新的摘要问题(称为多方面比较摘要)。PI将开发通用的概率方法，可应用于不同领域的问题的多个实例。其基本思想是使用概率混合模型来建模和提取待摘要文本集合中每个面的多个面和多个视图，然后使用提取的面和视图来生成面标签和为不同的面-视图组合选择句子。本文的研究将为文本摘要的研究开辟新的方向。开发的方法可以直接应用于在Web上为所有人提供更有用的产品意见检索服务，帮助多领域的科学家更有效地消化科学文献，从而加快科学发现的速度。所有研究资源和成果将通过出版物和可下载的软件和数据传播给研究界。研究成果还将加强当前的信息检索课程，从而改善信息技术工作人员的教育。

Text summarization generates a concise summary of documents tohelp a user quickly digest information and has become increasingly important due to the explosive growth of information. Most existing summarization methods can only generate an unstructured summary with a simple list of sentences. In many applications, however, we want to generate a (more structured)multi-faceted comparative summary, in which sentences are grouped into multiple facets and compared across different views. For example, a summary about laptop opinions may group sentences into facets such as ``battery life'' and ``memory'' and separate sentenceswith positive and negative opinions in each facet. This project aims to systematically study this new summarization problem (called multi-facetedcomparative summarization). The PI will develop general probabilistic approaches that can be applied to multiple instances of the problem in different domains. The basic idea is to use probabilistic mixture models to model and extractthe multiple facets and multiple views of each facet in a set of text documents to be summarized.The extracted facets and views are then used to generate facet labels and select sentences fordifferent facet-view combinations. The proposed research will open up a new research direction in text summarization. The developed methods can be directly applied to provide more usefulproduct opinion retrieval service on the Web to all people and help scientists in multiple fieldsto digest scientific literature more effectively, thus speeding up scientific discovery.All research resources and results will be disseminated to the research community through publications and downloadable software and data. The research results will alsoenhance the current curriculum in information retrieval, leading to improved education of the information technology workforce.