CAREER: Neural Transcript Summarization and Induction of Document Structure

音频和视频的爆炸性覆盖范围和力量，再加上准确的字幕，正在扩大对大量文字记录集合的获取。自动笔录摘要能够从音频和视频记录的笔录生成文本摘要。它为许多拥有大量成绩单的行业带来了希望，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。无论是共享会议记录还是快速记录实况流录音，使用文字记录摘要工具都是将语音录音转换为文本摘要的劳动密集型任务外包的可行方法。尽管从业者渴望总结各种各样的成绩单，但他们无法处理口语的复杂性。如果没有强大的摘要技术，用户可能会被可用的信息量淹没，无法有效地定位重要主题。该职业项目的研究目标是通过研究摘要中的基本问题，为自动摘要提供一个统一的方法框架，以提高摘要内容选择和生成的效率。拟议的工作将利用深层神经网络和语言结构预测的能力，以归纳文字记录的文件结构，并能够制作全面的摘要。研究计划的具体目标是：(A)揭示非正式、冗长的自发发言笔录的结构；(B)制作全面的摘要，使用户能够轻松浏览笔录；(C)建立一项结合内在和外在措施的评价方案，以评估笔录摘要的质量。该项目力求解决笔录摘要方面的根本挑战，为外地从业人员提供可靠、普遍可用的摘要解决方案。这项研究计划将被完全整合到一个协同教育计划中，让不同的学生参与总结技术的探索。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.