CAREER: Neural Transcript Summarization and Induction of Document Structure

音频和视频的爆炸性影响和力量，加上准确的字幕，正在扩大对大量文字记录的访问。自动成绩单摘要允许从音频和视频记录的成绩单中生成文本摘要。它为许多拥有大量记录的行业带来了希望，从远程医疗和远程医疗，金融服务，视频会议到播客和直播服务提供商。无论是需要分享会议记录还是快速记录直播录音，使用转录摘要工具都是一种可行的方式，可以将将语音记录转换为文本摘要的劳动密集型任务外包。虽然练习者渴望总结各种各样的文字记录，但他们无法处理口语的复杂性。如果没有强大的摘要技术，用户可能会被大量的信息淹没，无法有效地确定重要的主题。本CAREER项目的研究目标是通过研究摘要中的基本问题，为自动成绩单摘要提供一个统一的方法框架，以提高内容选择和成绩单摘要制作的效率。拟议的工作将利用深度神经网络和语言结构预测的力量，在成绩单上诱导文档结构，并生成全面的摘要。研究计划的具体目标是：（a）发现自发性言语的非正式、冗长的转录本的结构，（B）产生全面的摘要，使用户能够轻松地浏览转录本，以及（c）建立一个评估协议，结合内在和外在的措施来评估转录本摘要的质量。该项目旨在解决成绩单摘要中的基本挑战，为现场从业人员提供强大的，普遍可访问的摘要解决方案。该研究计划将被完全整合到一个协同教育计划中，以吸引不同的学生学习者探索摘要技术。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估来支持。

The exploding reach and power of audio and video, combined with accurate captioning, is broadening access to large collections of transcripts. Automatic transcript summarization enables the production of textual summaries from transcripts of audio and video recordings. It holds promise for numerous industries that have large collections of transcripts, ranging from telehealth and telemedicine, financial services, video conferencing, to podcast and livestream service providers. Whether one needs to share the minutes of a meeting or quickly take notes of a livestream recording, using a transcript summarization tool is a viable way to outsource the otherwise labor-intensive task of turning voice recordings into textual summaries. Though practitioners are eager to summarize transcripts of various sorts, they cannot deal with the complexities of spoken language. Without robust summarization technology, users can become overwhelmed by the amount of information available and fail to effectively pinpoint topics of importance.The research goal of this CAREER project is to provide a unified methodological framework for automatic transcript summarization through investigation of fundamental problems in summarization to improve the efficiency of content selection and production of transcript summaries. The proposed effort will harness the power of deep neural networks and linguistic structure prediction to induce document structure on transcripts and enable the production of comprehensive summaries. Specific objectives of the research plan are to (a) uncover the structure of informal, verbose transcripts of spontaneous speech, (b) produce comprehensive summaries to allow users to navigate the transcripts with ease, and (c) establish an evaluation protocol that combines intrinsic and extrinsic measures to assess the quality of transcript summaries. The project seeks to address fundamental challenges in transcript summarization to provide robust, universally accessible summarization solutions to field practitioners. The research plan will be fully integrated into a synergistic education plan to engage diverse student learners in exploration of summarization technology.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.