CRII:RI:Toward Socially-diverse Multi-document Summarization

用户生成的社交数据，如微博、讨论帖子和产品评论，通常包含丰富的意见和想法表达、近乎实时的信息，以及从健康到社会政治运动等广泛主题的不同观点。这些数据代表了来自各行各业和属于不同社会群体的人们的许多独特观点。检索这些数据相对容易;然而，从如此大量的非结构化、嘈杂和冗余数据中提取重要和有意义的信息以获得整体视图仍然极具挑战性。自动文本摘要模型是一种重要的计算工具，它通过将多个文档压缩成简短的摘要来识别文本的突出部分，大多数算法都专注于提高摘要沿着流畅性，真实性，连贯性等方面的质量。然而，当涉及到汇总社会多样性数据时，质量的一个重要但被忽视的方面是摘要所包含的不同观点的广度。本研究旨在改进面向社会丰富数据的文本摘要算法，并特别受到自然语言处理应用的启发。本项目将利用社会多样性（社会数据的独特特性）来提高多文档摘要的整体质量。该项目的技术目标分为两个方面。第一个重点是开发一套新的摘要数据集，这些数据集对应于来自不同社会显著群体的多个文档，可以作为模型训练和评估的可靠基准。第二个重点是开发新的无监督和少量学习算法，用于生成高质量的提取和抽象摘要，反映广泛的不同观点。这一目标将开发新方法，将多样性约束集成到摘要算法中，并联合优化摘要的文本质量和社会多样性。新的方法将补充一个全面的评估计划，包括自动和人工评估的几个总结指标。这项研究工作将创造跨学科的机会，将来自语言技术和公平社区的研究人员和学生聚集在一起，开发新的数据集和算法，以生成社会多样性摘要。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。

User-generated social data such as microblogs, discussion posts, and product reviews often contain rich expressions of opinions and thoughts, near real-time information, and diverse perspectives on a wide range of topics from health to socio-political movements. Such data represents many unique perspectives originating from people from many different walks of life and belonging to different social groups. Accessing such data is relatively easy; however, extracting important and meaningful information to get a holistic view from such an overwhelming volume of unstructured, noisy, and redundant data remains extremely challenging. Automatic text summarization models are important computational tools that enable identification of salient pieces of text by condensing several documents into a short, concise summary, with most algorithms focusing on improving the quality of the summaries along dimensions of fluency, factuality, coherence and so on. However, when it comes to summarizing socially diverse data, an important yet overlooked dimension of quality is the breadth of diverse perspectives that a summary encapsulates. This research aims to improve text summarization algorithms for socially rich data and is specifically motivated by applications in natural language processing.This project will leverage social diversity, a unique characteristic of social data, for improving the overall quality of multi-document summaries. The technical aims of the project are divided into two thrusts. The first thrust develops a suite of novel datasets of summaries corresponding to multiple documents from different socially salient groups that can serve as reliable benchmarks for model training and evaluation. The second thrust develops new unsupervised and few-shot learning algorithms for generating high quality extractive and abstractive summaries reflecting a broad spectrum of diverse perspectives. This thrust will develop new methods for integrating diversity constraints into summarization algorithms as well as jointly optimizing summary’s textual quality and social diversity. The novel methods will be complemented by a comprehensive evaluation plan including several summarization metrics of automatic and human evaluation. This research effort will create cross-disciplinary opportunities for bringing together researchers and students from language technology and fairness communities to develop new datasets and algorithms for generating socially diverse summaries.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.