CAREER: Neural Transcript Summarization and Induction of Document Structure

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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.