Summarizing text-rich documents has been long studied in the literature, but most of the existing efforts have been made to summarize a static and predefined multi-document set. With the rapid development of online platforms for generating and distributing text-rich documents, there arises an urgent need for continuously summarizing dynamically evolving multi-document sets where the composition of documents and sets is changing over time. This is especially challenging as the summarization should be not only effective in incorporating relevant, novel, and distinctive information from each concurrent multi-document set, but also efficient in serving online applications. In this work, we propose a new summarization problem, Evolving Multi-Document sets stream Summarization (EMDS), and introduce a novel unsupervised algorithm PDSum with the idea of prototype-driven continuous summarization. PDSum builds a lightweight prototype of each multi-document set and exploits it to adapt to new documents while preserving accumulated knowledge from previous documents. To update new summaries, the most representative sentences for each multi-document set are extracted by measuring their similarities to the prototypes. A thorough evaluation with real multi-document sets streams demonstrates that PDSum outperforms state-of-the-art unsupervised multi-document summarization algorithms in EMDS in terms of relevance, novelty, and distinctiveness and is also robust to various evaluation settings.

翻译：文本丰富的文档摘要长期以来一直是文献中研究的课题，但现有工作大多集中于对静态且预定义的多文档集合进行摘要。随着在线平台生成和分发文本丰富文档的快速发展，持续摘要的动态演变多文档集合（其中文档和集合的组成随时间变化）的需求日益迫切。这一挑战尤其在于摘要不仅要有效整合来自每个并发多文档集合的相关、新颖和独特信息，还要高效服务于在线应用。本研究提出一个新的摘要问题——演变多文档集合流摘要（EMDS），并引入一种新颖的无监督算法PDSum，其核心思想是原型驱动的持续摘要。PDSum为每个多文档集合构建轻量级原型，并利用该原型适应新文档，同时保留先前文档积累的知识。为了更新新摘要，通过测量每个多文档集合中最具代表性句子与原型的相似性来提取这些句子。通过真实多文档集合流的全面评估，结果表明PDSum在相关性、新颖性和独特性方面优于EMDS中当前最先进的无监督多文档摘要算法，并且对各种评估设置具有鲁棒性。