PDSum: Prototype-driven Continuous Summarization of Evolving Multi-document Sets Stream
PDSum: Prototype-driven Continuous Summarization of Evolving Multi-document Sets Stream
复制标题
DOI：
10.1145/3543507.3583371
复制
发表时间：
2023-02
期刊：
影响因子：
--
通讯作者：
Susik Yoon;Hou Pong Chan;Jiawei Han
中科院分区：
文献类型：
--
作者：
Susik Yoon;Hou Pong Chan;Jiawei Han
长期以来，文献中一直在研究富文本文档的摘要，但现有的大多数努力都是为了摘要一个静态的和预定义的多文档集。随着生成和分发富文本文档的在线平台的快速发展，迫切需要不断总结动态演变的多文档集合，其中文档和集合的组成随着时间的推移而变化。这尤其具有挑战性，因为摘要不仅应有效地合并来自每个并发多文档集的相关、新颖和独特的信息，而且还应有效地为在线应用程序提供服务。本文提出了一种新的文摘问题--进化多文档集流文摘(EMDS)，并结合原型驱动的连续文摘思想，提出了一种新的无监督算法PDSum。PDSum为每个多文档集构建了一个轻量级原型，并利用它来适应新文档，同时保留从以前文档中积累的知识。为了更新新的摘要，通过测量它们与原型的相似性来提取每个多文档集最具代表性的句子。对真实多文档集流的全面评估表明，PDSum在相关性、新颖性和独特性方面优于EMDS中最先进的无监督多文档摘要算法，并且对不同的评估设置具有较强的鲁棒性。
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.