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.

在一个以压倒性速度生产长文档的时代，读者甚至没有时间浏览文档来决定哪些主题值得详细的外观。该职业项目的目标是构建可以从长文档中理解和汇总信息的文本摘要系统，以便用户使用他们喜欢的样式生成的摘要来探索其内容。摘要工具将使长文档更容易访问和可理解，从而减少公众的知识学习经验。研究人员和从业人员还可以使用这些工具来总结与他们的工作相关的长文档，并且教育工作者可以将它们纳入课堂上，以增强学生的阅读和写作技巧。该项目还扩大了调查人员使年轻学生参与沉浸式研究机会的努力，使他们能够参与高级摘要系统的设计和实施。该项目为长文档开发了一个新的摘要框架，其中文章级的摘要摘要提供了概述，而问题及属于问题的层次结构介绍了不同级别的细节。该项目的技术贡献是三倍。首先，通过使用自适应预测的稀疏注意并用知识编码器增强，最新摘要（例如变压器）的二次时间复杂性（例如变压器）会降低。其次，一个开放式的问题生成模型填充了自动学习的问题模板，以产生问题 - 苏格尔层次结构中相干的具体问题。第三，摘要是通过一代迭代调整为用户指定样式量身定制的，这反映了普通语言指南中的重要建议。该项目通过从政府报告中收集的新数据集进行了实验，因为它们的长度，主题多样性和公式化的词汇体现了长期文档摘要的许多常见挑战。还设计了新的评估方法，并有披oo的问题来针对常见的错误世代，并以模型的置信度指标来指明错误而无需使用参考。该奖项反映了NSF的法定任务，并被认为是通过基金会的智力优点和更广泛影响的评估来通过评估来获得的支持。