CRII: RI: Towards Abstractive Summarization of Meetings

会议是合作、共享信息和交换意见的一种常见方式。然而，许多现有的会议记录都很长，没有条理，因此很难浏览。对于用户来说，通过阅读完整的文字记录来获取重要的会议产出将非常耗时。因此，通过提供对过去会议的基本内容的快速访问，自动生成的会议摘要对个人和企业都具有重要价值。该研究项目的核心目标是自动生成摘要风格的重点会议摘要，以帮助用户轻松地消化海量的会议内容。它有助于研究界更好地了解会议领域的特征，以更一致的方式定义会议中的摘要任务，改进语音摘要评估指标，并允许在许多应用程序中广泛使用语音摘要技术(如生成会议纪要或讲座大纲)。这个项目的更广泛的影响包括与社会科学家分享对对话文本的见解，为学生提供自然语言处理研究培训，以及为公众提供有效的会议摘要方法。本研究项目旨在通过开发重要结果识别和自然语言摘要生成的计算模型来构建会议摘要，以及设计客观的摘要评估方法。现有的会议摘要系统在很大程度上仍然是摘录的：它们的摘要完全由直接从要摘要的会议中挑选的发言拼凑而成。虽然提炼法相对容易构建，但它只是将一组话语作为最终的总结，而不能产生简洁和可读的总结，这主要是由于口语对话的自发性质。该项目制定了一个新的框架，说明会议的特殊方面，并利用这些框架确定包含重要成果的发言。利用具有丰富特征训练的基于判别学习的潜变量模型，联合捕捉话题转移，提取具有重要输出的话语。为了在一个过程中完成句子规划和表层实现，提出了一种基于神经网络的自然语言生成模型。客观评估方法旨在衡量生成摘要的质量的各个方面。

Meeting is a common way to collaborate, share information and exchange opinions. Many available meeting transcripts, however, are lengthy, unstructured, and thus difficult to navigate. It would be time-consuming for users to access important meeting output by reading the full transcripts. Consequently, automatically generated meeting summaries is of great value to people and businesses alike by providing quick access to the essential content of past meetings. The core objective of this research project is to automatically generate abstract-style focused meeting summaries to help users digest the vast amount of meeting content in an easy manner. It helps the research community to better understand the characteristics of the meeting domain, define the summarization task in meetings in a more consistent way, improve speech summarization evaluation metrics, and allow the wide use of speech summarization techniques in many applications (such as generating meeting minutes or lecture outlines). The broader impacts of this project includes sharing insights on conversational text with social scientists, providing natural language processing research training to students, and contributing effective methods for meeting summarization to the general public.This research project aims at constructing abstractive summaries of meetings by developing computational models for important outcome identification and natural language summary generation, as well as designing objective summary evaluation methods. Existing meeting summarization systems remain largely extractive: Their summaries are comprised exclusively of patchworks of utterances selected directly from the meetings to be summarized. Although relatively easy to construct, extractive approaches simply present a set of utterances as the final summary, and fall short of producing concise and readable summaries, largely due to the spontaneous nature of spoken dialogue. This project formulates a new framework that accounts for the special aspects of meetings and use them to identify the utterances that contain important outcomes. A discriminative learning-based latent variable model trained with rich features is utilized to jointly capture topic shifting and extract utterances with important outputs. To perform sentence planning and surface realization in one single process, a neural network-based natural language generation model is developed. Objective evaluation methods are designed to measure various aspects for the quality of generated summaries.