CAREER: Long Document Summarization with Question-Summary Hierarchy and User Preference Control

职业：具有问题摘要层次结构和用户偏好控制的长文档摘要

基本信息

批准号：
2046016
负责人：
Lu Wang
金额：
$ 54.76万
依托单位：
Regents of the University of Michigan - Ann Arbor
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2021
资助国家：
美国
起止时间：
2021-07-01 至 2026-06-30
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2046016&HistoricalAwards=false
关键词：
CAREER Long Document Summarization Question

项目摘要

In an era when long documents are produced at an overwhelming speed, a reader may not have time even to skim over a document to decide which topics deserve a detailed look. The goal of this CAREER project is to build text summarization systems that can understand and aggregate information from long documents, so as to allow users to explore their content with summaries that are generated in styles they prefer. The summarization tools will make long documents more accessible and comprehensible, easing the knowledge learning experience of the general public. Researchers and practitioners can also use the tools to summarize long documents relevant to their work, and educators can incorporate them in their classes to bolster students' reading and writing skills. The project also broadens the investigator’s efforts of engaging young students in immersive research opportunities, allowing them to participate in the design and implementation of advanced summarization systems. This project develops a new summarization framework for long documents in which article-level abstractive summaries provide an overview, and a question-summary hierarchy presents different levels of details. The technical contributions of this project are three-fold. First, the quadratic time complexity of state-of-the-art summarization (e.g., Transformer) is reduced by using adaptively predicted sparse attentions and augmented with a knowledge encoder. Second, an open-ended question generation model fills automatically learned question templates to produce concrete questions that are coherent within the question-summary hierarchy. Third, summaries are tailored to user-specified styles via iterative adjustments during generation, reflecting important advice in plain-language guidelines. This project experiments with new datasets collected from government reports, since their length, topic diversity, and formulaic verbiage embody many common challenges for long document summarization. New evaluation methods are also designed, with cloze questions to target common erroneous generations, and with model confidence metrics to pinpoint errors without using references.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.

在一个长文档以惊人的速度生成的时代，读者甚至可能没有时间浏览文档来决定哪些主题值得详细查看。这个 CAREER 项目的目标是构建能够理解和理解的文本摘要系统。聚合长文档中的信息，以便用户能够以自己喜欢的方式生成摘要来探索其内容。摘要工具将使长文档更易于理解和理解，从而简化公众研究人员和从业者的知识学习体验。还可以使用总结与其工作相关的长篇文档的工具，教育工作者可以将它们纳入课堂，以提高学生的阅读和写作技能。该项目还扩大了研究者让年轻学生参与沉浸式研究机会的努力，使他们能够参与到研究中。高级摘要系统的设计和实现。该项目为长文档开发了一个新的摘要框架，其中文章级摘要摘要提供了概述，而问题摘要层次结构则呈现了不同级别的细节。折叠。首先，通过使用自适应预测的稀疏注意力来降低最先进的摘要（例如 Transformer）的二次时间复杂度，并通过知识编码器进行增强。其次，开放式问题生成模型将自动学习的问题模板填充到其中。第三，通过在生成过程中进行迭代调整，生成符合用户指定风格的具体问题，反映了该项目使用新数据集的重要建议。从政府报告中收集的数据，因为它们的长度、主题多样性和正式措辞体现了长文档摘要的许多常见挑战，还设计了新的评估方法，通过完形填空问题来针对常见的错误生成，并通过模型置信度指标来查明错误，而无需使用。该奖项反映了 NSF 的法定使命，并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，被认为值得支持。