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，往往包含丰富的观点和思想表达、接近实时的信息，以及对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。访问这些数据相对容易；然而，从如此庞大的非结构化、嘈杂和冗余数据中提取重要且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，它可以通过将几个文档压缩成一个简短的摘要来识别重要的文本片段，大多数算法都侧重于提高摘要的质量，包括流畅性、真实性、连贯性等方面。然而，在总结社会多样性数据时，一个重要但被忽视的质量维度是总结所包含的不同视角的广度。本研究旨在改进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。该项目将利用社会多样性这一社会数据的独特特征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重点。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突出群体的多个文档，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镜头学习算法，用于生成高质量的提取和抽象摘要，反映了广泛的不同观点。这一推力将开发将多样性约束整合到摘要算法中的新方法，并共同优化摘要的文本质量和社会多样性。新方法将由一个综合评估计划补充，包括几个自动和人工评估的汇总指标。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，以生成社会多样化的摘要。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.