CAREER: Using Rich Information from Speech and Text for Meeting Summarization

文本摘要和语音识别的最新进展并没有被语音摘要的类似进展所取代。本次会议总结项目有三个重点。首先，它研究了两种不同的摘要任务定义，通用提取摘要和基于查询的摘要。其次，它解决了简单地将文本摘要技术应用于语音识别输出时出现的核心挑战。它评估低级别结构信息（如句子边界和不流利）的影响，使用高级别会议结构信息（如主题和会议结构，扬声器交互），并使用丰富的识别输出（识别假设，n-best和格子中的置信度测量）进行摘要。最后，使用各种测量来评估摘要方法的有效性，包括与人类摘要参考、外部度量（例如，该项目采用先进的算法，将来自语音和文本的动机良好的丰富信息联合收割机结合起来，用于会议摘要。一个重要的成果将是会议领域的总结任务的有用性和新的方法来衡量这项任务的成功发展的调查结果。这项工作将推进我们对人类互动的理解，并提高我们自动处理人类语音的能力。本项目中开发的附加说明的数据和评估工具将与社区共享。该项目是多学科的，涉及语音处理，自然语言处理和会话分析。研究和教育的紧密结合将大大提高下一代研究人员的卓越性。

Recent advances in text summarization and speech recognition have not been paralleled by similar advances in speech summarization. This project on meeting summarization has three focuses. First, it investigates two different summarization task definitions, generic extractive summarization, and query-based summarization. Second, it addresses the core challenges that arise when simply applying text summarization techniques to speech recognition output. It evaluates the impact of low-level structural information (such as sentence boundaries and disfluencies), uses high-level meeting structural information (such as topics and meeting structure, speaker interaction), and uses rich recognition output (confidence measures in the recognition hypotheses, n-best and lattices) for summarization. Finally, various measurements are used to evaluate the effectiveness of summarization approaches, including comparing to human summary references, extrinsic metrics (e.g., based on a question-answering task), and human evaluation for the usefulness of the query-based summaries.This project employs advanced algorithms to combine well-motivated rich information from both speech and text for meeting summarization. An important outcome will be the findings about the usefulness of the summarization task for the meeting domain and development of new approaches to measuring success for this task. This work will advance the frontier of our understanding of human interactions and improve our ability to automatically process human speech. The annotated data and evaluation tools developed in this project will be shared with the community. This project is multidisciplinary, involving speech processing, natural language processing, and conversation analysis. The tight integration of research and education will significantly enhance the excellence of next-generation researchers.